Wood veneer defect detection plays a vital role in the wood veneer production industry. Studies on wood veneer defect detection usually focused on detection accuracy for industrial applications but ignored algorithm execution speed; thus, their methods do not meet the required speed of online detection. In this paper, a new detection method is proposed that achieves high accuracy and a suitable speed for online production. Firstly, 2838 wood veneer images were collected using data collection equipment developed in the laboratory and labeled by experienced workers from a wood company. Then, an integrated model, glance multiple channel mask region convolution neural network (R-CNN), was constructed to detect wood veneer defects, which included a glance network and a multiple channel mask R-CNN. Neural network architect search technology was used to automatically construct the glance network with the lowest number of floating-point operations to pick out potential defect images out of numerous original wood veneer images. A genetic algorithm was used to merge the intermediate features extracted by the glance network. Multi-Channel Mask R-CNN was then used to classify and locate the defects. The experimental results show that the proposed method achieves a 98.70% overall classification accuracy and a 95.31% mean average precision, and only 2.5 s was needed to detect a batch of 50 standard images and 50 defective images. Compared with other wood veneer defect detection methods, the proposed method is more accurate and faster. Wood is an essential natural resource, but defects on wood products can seriously affect the commercial value. Due to the low quality of raw materials and inappropriate manufacturing processes, there are various kinds of defects on wood veneers, such as live knots, dead knots, and cracks. These defects diminish the utilization of raw wood materials in some developing countries. Visual quality inspections are still mainly performed by trained workers in the wood processing industry ,2.A fast wood defect detection method is necessary for modern wood veneer processing industries to improve their wood use rate and increase their revenue. Currently, many kinds of technologies have been used to detect defects on wood veneers, including air-coupled ultrasonic technology , stress With the development of computer hardware, especially high-performance Graphics Processing Units (GPUs), the combination of computer vision technology and deep learning technology has the advantages of fast speed and low cost, and has been widely used in defect detection . Park etMask R-CNN was developed as an improved version of faster R-CNN, which can detect defects and output the defect shapes; it has been widely used in the field of target detection, and achieves excellent performance in many applications. In mask R-CNN, the image is processed by a series of convolution layers and passed through three different branches of networks . HoweverIn this study, a glance network was added before the mask R-CNN to reduce the running time of the mask R-CNN. The glance network was used to filter out all defective pictures and mistake as few regular pictures for defective pictures as possible. Simultaneously, the glance network should use as few computing resources as possible during classification. Although the goals of the glance network were clear, it was hard to find the most suitable architecture and parameters to satisfy them. Hence, neural network architecture search (NAS) technology was used to construct the structure and determine the parameters of the glance network.NAS is an emerging technology that constructs different kinds of structures based on intelligent algorithms ,26,27. AIn this research, an improved method that combined NAS technology and mask R-CNN was applied to detect defects on wood veneer surfaces and output the shape and type of defects. The proposed algorithm integrates an optimized multi-channel mask R-CNN and a glance network based on NAS to obtain a fast scan of the input image for distinguishing defective wood veneer images from all wood veneer images. In contrast to standard NAS technology, in the glance network, the speed of the neural network is considered using the value of floating point operations (FLOP). Then, the glance network established by the NAS extracts and outputs the first impression features of defect images to the following multi-channel mask R-CNN for further detection. To have the best combination of features input into the multi-channel mask R-CNN, a genetic algorithm is used to optimize the feature selection for achieving better detection results. The proposed integrated model can ensure detection accuracy and better real-time performance compared to the traditional mask R-CNN, which lacks a glance network and a multi-channel structure.A glance network is developed for quickly scanning the image to determine whether the image is suitable for further detection, which significantly reduces detection time for industrial usage. Additionally, a new fit value function utilizing the FLOP of the network is constructed for the first time to improve the real-time performance of the glance network.A genetic algorithm is used to determine the feature selection of the multi-channel mask R-CNN input channels in order to achieve higher detection accuracy.Specifically, the integrated model is used as the final classifier to identify the defects on the wood veneer surface, and the specific contributions of this work are as follows:Wood veneer images were collected using acquisition equipment as shown in The photoelectric sensor is ES12-D15NK produced by LanHon in Shanghai, China, and its detection distance is up to 15 cm. It was attached to the gap of the belt and produced a pulse to the acquisition board on the computer. When the wood veneer was detected to reach the gap, the two cameras started scanning the wood veneer. The scanning process stopped when the wood veneer leaves the gap. In this way, the two sides of the wood veneer were obtained in one scanning process. The experimental wood discussed in this paper was pieces of Chinese fir with dimensions 1000 × 100 × 10 mm, which were picked out by trained workers from a wood processing company . A scanning photo of a wood veneer including the background had the dimensions 18,000 × 2048 × 3 (width × height × channels), as shown in 2.To construct an appropriate dataset for experiments, the black background from the raw pictures was first removed. Then, the pictures were cut into 200 × 200 pixel pieces. After that, the pictures were processed by rotation, magnification, and horizontal and vertical mirroring to expand the quantity of the wood veneer image set. Finally, 2838 wood veneer pictures were obtained, of which 612 were regular pictures (background), and the remaining 2226 pictures each contained one or more defects, including 846 live knot pictures, 760 dead knot pictures, and 620 crack pictures, as shown in Trained workers used LabelMe software to label wood defects. LabelMe is a graphical image annotation tool written in Python that uses Qt as its graphical interface. LabelMe can carry out polygon annotation and output a COCO-format dataset, such as segmentation, which is helpful for picking wood defect masks from the images. The label results of defect images are shown in The process of the proposed target detection algorithm is shown in In the real industrial wood detecting process, wood defects only account for a small part of the whole wood, as shown in A. The architecture is passed to a performance estimation strategy, which returns the estimated performance of a to the search strategy.To achieve accurate detection performance, NAS technology was used to design an appropriate structure for the glance network. Deep learning was successfully applied in perceptual tasks with its advantage of automatic feature extraction from data rather than by manual design . NAS is In this study, a coding method was developed to determine the search space of the glance network architecture. Here, the function of the glance network was to analyze the input picture as fast as possible based on a convolution network, which has been successful in many applications ,38,39,40,After determining the search space for the network architecture, the network was limited to a fixed depth, but there were still many candidate networks. Therefore, a suitable search strategy was necessary to effectively search for the desired glance network in the vast search space. The search strategy of the glance network structure not only has to find the architecture with excellent performance but should also avoid premature convergence to a sub-optimal architecture area. Here, the search strategy is transformed as a two-level optimization problem :(1)minNαTo solve the two-level optimization problem, a genetic algorithm was used for the optimization of ications ,43,44,45For industrial applications, both the accuracy and the speed of the proposed model were considered by designing a new kind of fitness value ( a model . The FLO a model :(4){FLOPAssume the maximum number of layers in the architecture space is After 66 h of neural architecture searching, the structure of the glance network was determined as shown in During the detection of the glance network, some features of images are already extracted from the images to construct the result. To apply these features to achieve higher accuracy for the following mask network, the previous features skipped by the glance network are merged with the input images. This method has little effect on the detection speed of mask R-CNN, but it effectively improves the detection accuracy, as shown in the results listed in The convolution layer can extract different features based on different kinds of convolution kernels, and the output of a convolution layer can be expressed as:Assuming that the latest convolution layer of glanced networks has The NAS method is used to search for a high-performance glance network as a front-end network, and then the defect images and first impressions are input into the mask R-CNN network to determine the type and precise location of wood defects in Multi-scale detection is becoming increasingly critical in target detection, especially for small targets, and feature pyramid network (FPN) is a well-designed multi-scale detection method. Therefore, mask R-CNN uses ResNet50 and FPN for further feature extraction following glance network classification. FPN uses feature maps in the form of a pyramid, integrating these feature maps efficiently through bottom-up, top-down, and lateral connections. It can effectively improve the detection accuracy without increasing the detection time.After the feature extraction, the region proposal network (RPN) is used to extract the ROI in the mask R-CNN. The RPN network outputs rectangular candidate regions that may have defects in the image. The RPN mainly generates multiple anchors on the original image, and then classifies and regresses the generated anchors. The time needed for the RPN network to generate candidate frames is short, significantly reducing the computing resources and achieving real-time detection of wood defects.Next, RoI Align is applied to derive smaller feature maps from the ROI, which was extracted from feature maps and RPN, and then these smaller feature maps are input to the FC layers. Finally, a softmax classifier and frame regression are used to obtain the type and location of the defect, respectively. RoI Align uses a bilinear interpolation algorithm to determine the feature value of each point in the original image interest area and then performs pooling and other operations. It makes the pixels in the original image, and the pixels in the feature map are entirely aligned, effectively avoiding the quantization operation of ROI pooling.At the end of the network, mask R-CNN merges a branch that uses the FCN to predict the target mask. The branch distinguishes between the foreground and background by creating a binary mask for each defect, and then uses the FCN to complete instance segmentation, which meets the requirements of the online wood defect detection.To construct and test the proposed defect detection model, the 2838 collected wood veneer pictures were divided into three parts including a training set, a testing set, and a validation set in a ratio of 3:1:1. The training set was used to train the constructed network, the testing set was used to evaluate the network, and the validation set was used for the final judgment. The training platform is shown in The searching process of the glance network is shown in The search space of the network was massive, and it was difficult to validate whether the result of the searched structure was the best possible. Hence, a comparative experiment was carried out to validate whether the searched result was the local optimization. To ensure that the detection rate of the glance network is suitable for as many kinds of applications as possible, an adjustable confidence rate is proposed, expressed as:Different kinds of convolution filters extract features from different perspectives, and these features of different channels after Maxpooling2D layer of Glance Network in To obtain the best combination of input features for the multi-channel mask R-CNN, the genetic algorithm was used to optimize the selection. Finally, Features 3–5 and 8 were selected and combined with the input feature maps into the multi-channel mask R-CNN. With the selected features, the accuracy of the proposed model reached 98.70%.An experiment was carried out to determine the influence of multi-channel features on speed performance, and the results are listed in In this research, the overall classification accuracy (OCA), mean average precision (MAP), and inference time for each batch were used as the evaluation indices of each model. The network classification evaluation indices are:(1)Improvement of the detection speed of the model: A glance network was designed at the front end of the multi-channel mask R-CNN, which was mainly used to classify regular wood and defective wood. The defective pictures were then picked out and transformed into mask R-CNN for further inspection. To obtain the most suitable architecture of the glance network for wood detection, NAS technology was used to determine the architecture and parameters of the glance network, and FLOPs were used for speed optimization in a NAS for the first time.(2)Improvement of the detection accuracy of wood defects: We fed the feature of the defective wood extracted by the glance network into the mask R-CNN. In addition, a genetic algorithm was used to optimize the selection of the feature channels to obtain the best combination of input features for the mask R-CNN.A GM-Mask R-CNN model was proposed in this paper for the detection of wood defects, and the experimental results showed that the GM-Mask R-CNN model exhibits excellent performance. The proposed model was used to identify three defects types, including dead knots, live knots, and cracks in the wood; the detection accuracy reached 98.7% and the mean average precision of the model reached 95.31%. Compared with traditional wood defect detection algorithms, the detection accuracy was significantly improved and the running time was reduced. The developed model offers two main improvements as follows:The experiment also provided us with some inspirations for future work. Firstly, the NAS is used to design an appropriate structure for the glance network, and this process will takes much time in the training process, especially when the network searching ranges further increases. We will try to design a more appropriate search strategy to accelerate the search process of glance network structure.Second, the proposed method is a combined model; the performance of the glance network has great influence on the subsequent classification accuracy of the multi-channel Mask R-CNN, but it is difficult to check which parts diminish the final performance more directly from the finally detective results, because both the glance network and multi-channel Mask R-CNN provide the abstract features to detect defects. Therefore, we will try to find out a joint optimization approach to reduce the whole complexity of the proposed method.Third, we will continue to try to improve the detection speed of the defect detection algorithm as much as possible and make it capable of detecting more different types of defects. Additionally, the proposed model will be used to calculate the area of the defect, which would lay a good foundation for the classification of wood defects. Algorithms that meet the needs of industrial production must be effectively combined with industrial equipment and put into industrial production.


Measurement of high-current pulses is crucial in some special applications, e.g., electrodynamic accelerators (EA) and converters. In such cases, the current shunts have limitations concerning the frequency bandwidth. To overcome the problem, a method based on the shunt mathematical model is proposed. In the method, the solution of ordinary differential equations for the RL circuit is carried out in order to obtain the real current shape. To check the method, as a referee, a Rogowski coil dedicated to measuring high-current pulses was used. Additionally, the measurement results were compared with the mathematical model of the tested power supply system. Measurements were made for the short power supply circuit, which allows eliminating the nonlinearity. The calculations were carried out using a circuit model. In order to obtain the parameters of the shunt (resistance and inductance), it was modeled using an ANSYS/Q3D Extractor software. Comparison of calculation and measurement results confirms the correctness of our method. In order to compare results, the normalized root mean square error (NRMSE) was used. Design or optimization of electrical devices requires formulation of the correct mathematical model. Thus, an important step in the research is the measurement verification of the obtained results ,2. MeasuMeasuring shunts enable precise measurement of the peak value and shape of the waveform of relatively fast current pulses. Current shunts are characterized by a wide frequency response, short rise time, and high accuracy of resistance values—they are made in the accuracy class from eral kHz ,7,8,9. Feral kHz . Variouseral kHz , the equeral kHz , a simulThere are many different works about Rogowski coils. Current measurements of them can be influenced by many factors ranging from their construction, the shape of the coil loop, and the location of the measured circuit in the coil loop ,14,15. AIn the presented paper, a comparison of both measuring methods for high-current pulses occurring in electrodynamic accelerators (EA) was carried out. A problem was observed with the shape of a current wave measured by the shunt—there was a relatively high step of current value, which is not typical for an RL load. This step in the current wave is not visible in the model of the system and in measurements using the Rogowski coil. Including the small inductance of the shunt in the mathematical model, the same sharp increase was observed. Based on theoretical and experimental investigations, a method for solving this problem was proposed. It uses a circuit model of the shunt in order to obtain the proper current wave based on the solution of an ordinary differential equation (ODE). The presented method corrects the aberration of the shunt reading caused by its inductance. The correction significantly improves the accuracy of the current wave measurement, which was shown in The mathematical model of the investigated current shunt is based on its simplified equivalent circuit presented in The circuit parameters of the model could be determined either by calculation or measurement methods. To determine parasitic parameters by calculations, it is necessary to use electromagnetic field simulation tools, such as ANSYS Maxwell 3D, FEMM, or Opera 3D, which are based on finite element analysis (FEA) techniques to solve the Maxwell differential field equations. However, when the geometry of the test object becomes more complex, full-field simulation is computationally intensive, the models themselves often show poor convergence, and results can be affected by significant errors. The Partial Equivalent Circuit Method (PEEC), which uses Maxwell’s integral equations instead of differential equations and analytically calculates inductance and capacitance based on geometry and material information, is a very common solution ,25,26. SDue to the very small value of the shunt passive inductance and capacitance, and due to complex geometry of the current shunt, they were determined using a mathematical model formulated in the ANSYS Q3D Extractor program. It is a tool dedicated for calculating parasitic parameters in any 2D or 3D objects. The software allows calculating RLCG parameters  depending on the given frequency . Q3D ExtFor numerical calculations of the power supply system dynamics, the circuit method was used ,30,31,32Due to the relatively small pulse spectrum (less than 1 kHz), the capacitance value was neglected. Thus, the ODE could be written in a more simple form,The knowledge of the shunt parameters allows for recovering the real current waveform based on the Equation . Knowingk-th step;k-th step;h—integration step equal to the sampling time.Due to its simplicity, the method allows us to determine the real current wave in real-time.The Rogowski coil was chosen as a referee for current wave measurement. It is characterized by high bandwidth and precision. The measurements were also compared with the calculation results of the shunt model and supply system. In order to compare the measurement results quantitatively, the normalized root mean square error (NRMSE) was used. It is described by the following equation :(4)NRMSEN—number of measurement points;ith point;ith point.Comparative analysis of the measurement methods was carried out using the real impulse power supply system of EA. As an energy source, a capacitor bank divided into three symmetric sections with a total capacity of 363 mF was used .For switching-on of each section, high-power thyristors were implemented (model T95-1900 from Kubara LAMINA Company) . Both voThe electric diagram of the test stand is presented in In circular .The induced voltage in a closed measuring coil is described by the following relationship:N—number of turns of the coil;The output voltage A dedicated data acquisition system was used in order to measure the voltage in the coil. The oscilloscope acquired the output voltage signal. In copper conductivity rods’ conductivity The current shunt is made of copper mounting bases and thrust rods. The following parameters were adopted for the calculations:The conductivity of rods is approximately 200 times lower compared with copper.The numerical model in the Q3D software is presented in In rods of the investigated current shunt, the current density distribution is homogenous b. In theThe calculated shunt resistance was verified experimentally. A value of In the first step to compare the waveforms from the Rogowski coil and the current shunt, measurement repeatability was tested. In In The measured current waveforms differ depending on the measurement method used. In the case of a current shunt, there is a more rapid slope of current increase, and hence higher amplitudes in relation to the Rogowski coil were observed. The short pulse, observed in the shunt measurement at he shunt .In The values of NRMSE for the waveforms presented in The mathematical model presented in chapter 2 was verified experimentally. In In nd 1.68% , which iA similar comparison between the model and measurement results is presented for the current shunt . In thisTaking into account the shunt inductance, a very good agreement between calculations and measurement results was obtained . The NRMnd 1.71% . The errComparing current waves obtained from the Rogowski coil and current shunt, a significant difference is visible . Based oThe expression  for recoThe Rogowski coil measurement was assumed as a referee. The values obtained from the shunt after implementing our method are characterized by a significantly lower error than those obtained directly from the voltage drop measurement . The NRMSE error for waveforms obtained after applying Equation  is betwend 1.35% . The errThe most important advantage of the presented method is its simplicity. The only problem is determining the inductance of the current shunt (the resistance is known). This could be done either by measurements or by magnetostatic field calculations using the finite element method (FEM). In the case of the field modeling, all dimensions of the shunt and all material properties must be known.In order to test the proposed method more deeply, calculations and measurements for a shunt, which differs from the previous one, were carried out. The outline and dimensions of it are presented in Similar to the previous measurement, the Rogowski coil results were assumed as a referee. The errors values are given in For a high-current pulse measurement (some kA) there are very significant differences between current waveforms obtained from a shunt and visible Rogowski coil. The proper current wave is measured by the Rogowski coil, while the shunt does not give the correct result. Both mathematical models and measurements show that this is mainly due to the inductance of the shunt. The value obtained from measurements  after applying the correction arising from Equation . Taking In order to explain problems with the measuring of high-current pulses using a current shunt, we used both numerical models and experimental investigations. The most difficult task was choosing an appropriate model of the shunt. In the presented case, the Q3D software was successfully implemented. The presented approach is very useful and not only allows us to explain problems with current pulses measurement, but also indicates the way to solve them.


Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model’s recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets. Synthetic Aperture Radar (SAR) features all-weather, long-range and large-scale detection performance. It can obtain high-resolution radar images under extremely low-visibility weather conditions, effectively identify ground camouflage and masking targets and is widely used in marine environment detection, terrain survey and military target recognition field. SAR Automatic Target Recognition (ATR) is a crucial technique for interpreting SAR target images, which can effectively improve the utilization efficiency of SAR targets images . HoweverBased on the imaging characteristics of SAR target images, researchers have conducted a lot of research on the SAR ATR algorithm. Traditional SAR target recognition methods mainly concentrate on the stage of feature extraction and the construction of a classifier. In the feature extraction phase, Principal Component Analysis (PCA) , IndepenCNN is capable of processing multi-dimensional data and has powerful representation learning capabilities, therefore having attracted the attention of many researchers. Since AlexNet  won Imag(1)An improved lightweight CNN model based on the Atrous convolution and Inception module is proposed. This model can obtain a rich global receptive field, effectively preventing the network from overfitting, and obtain high recognition accuracy on the MSTAR dataset.(2)The Atrous-Inception module is designed to extract more detailed target feature information, and it has strong robustness on a small sample dataset of the constructed SAR targets images.(3)The transfer learning strategy is used to explore the performance of the prior knowledge based on optical, non-optical, hybrid optical and non-optical fields transferred to the SAR targets images recognition tasks, further improving the robustness as well as the generalization of the model on the SAR small sample datasets.In view of the above challenges, a lightweight network architecture TAI-SARNET combined with transfer learning is put forward here to achieve efficient SAR target image recognition. Firstly, the Atrous-Inception module with a small size convolution kernel is adopted in the network to increase both the depth and width of the network while increasing the receptive field and reducing the number of parameters. Secondly, Batch Normalization (BN)  is emploThe main work arrangement of this paper is as follows: The second part introduces the related work of CNN and transfer learning. The third part introduces the methods in this paper. The fourth part introduces the experimental results and analyzes them. The fifth part draws the conclusion.CNN is a feed-forward neural network with a deep structure that contains one or more convolution operations, enabling the transformation of mapping input to output. Compared with other neural networks, CNN’s most significant feature is the introduction of the concept of local connection and parameter sharing. Local connection can greatly reduce the amount of network model parameters, speed up model convergence and reduce the need for computing hardware. Parameter sharing refers to the convolution kernel with the same parameters to extract the features on the image multiple times, and the extracted features are combined non-linearly, so that the network can automatically extract better features. The above characteristics break through the limitations of traditional machine learning algorithms based on artificial design features and are widely used in many fields.GoogleNet marks a significant milestone in the history of CNN. It first proposes the Inception structure, which performs convolution and pooling operations upon input images in parallel and increases the adaptability of the network to multiple scales. At the same time, GoogleNet adopts the bottleneck layer design in the Inception module to achieve feature reduction, improves the local perception area of CNN and reduces the calculation of network parameters. Subsequently, Inception-v2 is proposed based on GoogleNet, and BN is introduced. On the one hand, the BN layer solves the problem of internal covariate shift and speeds up training. On the other hand, the BN layer plays the role of regularization, which effectively prevents the phenomenon of network overfitting. After that, Inception-v3  introducTraditional machine learning algorithms presume that training data and testing data follow the same data characteristic spatial and data characteristic distribution, but this assumption usually fails to meet the actual application scenario. In some specific areas, where data accessibility and labeling costs are difficult to obtain sufficient training data, transfer learning provides a solution to this situation . It starTransfer learning breaks through the limitations of traditional machine learning algorithms, allowing the task or data distribution of the source domain as well as the target domain to differ greatly, and effectively alleviate the lack of labeled data for the target tasks. The advantages of transfer learning are obvious, and researchers have also applied transfer learning to SAR target recognition tasks. Huang et al.  uses a pIn this section, we propose a lightweight SAR target image classification network combining the atrous convolution and the Inception module, and the application of transfer learning is introduced in this paper. The first part displays the basic network architecture. The second part illustrates the Atrous-Inception module in detail and elaborates on the related concepts of the receptive fields. The third part derives the mathematical formula of the design principle of the optimization algorithm used in this paper. In the fourth part, we give the specific definition of transfer learning and introduce the transfer learning strategy adopted in this paper.This paper presents a lightweight network based on atrous convolution and Inception-v3, which we call TAI-SARNET, the specific network structure is presented in The first four layers of TAI-SARNET are consistent with Inception-v3, and output 80 feature maps after passing the first four layers of the network. Then, an Atrous-Inception module is immediately followed, which contains three atrous convolution layers and outputs 96 map features, and details of The Atrous-Inception module will be introduced in Subsequently, the global average pooling layer is utilized in place of the traditional fully connected layer so as to realize the calculation of all pixel averages for each feature map output from the feature extraction part. The global average pooling layer is employed to directly reduce the data dimension, greatly reducing network parameters and play the role of regularization in the entire structure to prevent network overfitting. Finally, the data values from the global average pooling layer are input into the classifier softmax. The essence of softmax is a normalized exponential function that is functionally normalized as the probability distribution value within the 0–1 interval, and the formula of the softmax layer can be represented as follows:In Equation (2), The receptive field is used to represent the receptive range of different neurons in the network to the original image. The larger the value of neuron receptive field is, the greater the range of the original image it can access, which means that it contains more global and higher-level semantic features. In traditional CNNs, a convolution operation is first used to extract feature maps, and then downsampling is performed by a pooling operation to increase the receptive field. However, this operation can result in the loss of the internal structure and spatial information of the target, and the information of the small target object can not be reconstructed. Atrous convolution  is a gooThe size of the receptive field of the standard convolution is pertinent to the size of the current layer of the convolution kernel, the moving step of the convolution kernel, and the size of the receptive field of the previous standard convolution. The receptive field calculation formula for standard convolution is defined as follows:For example, if the first layer is a 3 Compared to the standard convolution, the atrous convolution has a hyperparameter called dilated rate, which refers to the number of intervals between convolution kernels. The calculation formula of the receptive field of the hollow convolution is as follows:The Equation (5) represents the dilated rate in the first atrous convolution layer, and the atrous convolution of the dilated rate of 1 is equivalent to a standard convolution. In the Inception series of networks, the strategy adopted is to use a small convolution kernel stack rather than a large convolution kernel, which is able to reduce the parameters while obtaining the receptive field of the same size as the large convolution kernel. However, expanding the receptive field by superimposing a small convolution kernel can only grow linearly, while atrous convolution can increase the receptive field exponentially without increasing the parameter amount. Therefore, the Atrous-Inception module is used to grow the receptive field, obtain a larger range of spatial information, and further control the number of network parameters. The detailed structure of the Atrous-Inception module is displayed in The gradient descent algorithm is a typical optimization method commonly used in CNNs, and the core of the backpropagation algorithm is to continuously use gradient descent to update the weight parameters of each layer for optimization. However, the gradient descent algorithm needs to traverse all samples per iteration, making the training process slow and taking a long time to reach convergence. The RMSProp algorithm is an adaptive learning rate algorithm propounded by Hinton , which uIn Equation (6), The calculation formula for parameter update is as follows:−8. The RMSProp algorithm is applied to update according to the parameter update rule of the above formula, one of the optimization learning algorithms commonly used in deep learning.In Equation (7), When the distribution of training data and testing data does not meet the prerequisites of the same distribution, traditional machine learning algorithms cannot achieve satisfactory performance. However, due to the cost of manual annotation and data accessibility, it is difficult to construct a qualified dataset from scratch. Transfer learning is capable of improving the learning effect of related new tasks through transferring knowledge from already learned tasks, breaking through the limitations of data distribution differences and the lack of a vast quantity of labeled data in the related target domain. Specifically, transfer learning transfers the knowledge learned from a source domain incorporating a large number of labeled training samples to a related target domain with a small number of labeled samples, so that the target domain achieves better results. In addition, transfer learning can reuse previous models, thus greatly speeding up learning efficiency. For more rigorous expressions, the relevant definitions and symbols of transfer learning are as follows: Given a domain For the optical domains, the source domain For the non-optical domains, the source domain For the purpose of fully comparing the transfer effects of data from different imaging modes as source domain knowledge, we take the classification task of combining optical and non-optical radar images as source task  dataset  and the In order to demonstrate the performance of the algorithm on SAR target recognition tasks, unified experimental verification is performed using the MSTAR dataset. The MSTAR dataset is the measured SAR ground stationary target data released by the MSTAR program and supported by the US Defense Advanced Research Projects Agency, which contains SAR target images obtained at various azimuths by multiple vehicle targets. The MSTAR dataset contains ten types of ground targets under Standard Operating Condition (SOC), including artillery , armored vehicle  and tank , where BRDM2 in the armored vehicle category contains three types of variants , T72 in the tank category contains three types of variants . The SAR target image resolution is 0.3 m × 0.3 m and the pixel size is 128 × 128, and the optical target image and the corresponding SAR targets image are presented in All experiments in this paper were run on a computer configured with AMD Ryzen 5 2600X processor, Nvidia GeForce RTX 2080 (8 GB) GPU and 32 GB RAM. The compilation environment of all experiments is unified to Ubuntu 18.04 system, and Keras deep learning framework developed by TensorFlow as the backend is used for training.−4, and 250 epochs are trained. The learning rate decay strategy is used during the experiment. The specific settings are:The optimizer used in this paper is RMSprop, the initial learning rate Monitoring indicators: validation loss, learning rate attenuation factor: factor = 0.5, waiting for transition rounds: patience = 20. When the monitored validation loss has not decreased after the number of patience rounds, the learning rate attenuation strategy will be triggered and the learning rate will be reset using Equation (8):The complete experimental process is expressed as follows: First, the image size of the MSTAR dataset under SOC is changed to 64 In this section, in order to explore the optimal network settings, different structures of TAI-SARNET will be tested on the dataset configuration mentioned in The dilated rate is one of the key factors exerting an impact on the receptive field of the atrous convolution layer, and it makes sense to explore the performance effect of different dilated rates on TAI-SARNET. As shown in The test results of TAI-SARNET embedded with different numbers of Atrous-Inception modules on the MSTAR dataset are shown in The operation objects of the merge layer are chiefly divided into two categories, one is to operate on the channels of the feature maps, and the other is to operate on the elements in the feature maps. The merge layer mode for channel operation is Concatenate, which connects multiple feature maps to be merged, eventually increasing the number of channels on the feature maps, but the amount of information on each feature map does not increase. The merge layer modes for elements operations include Add, Multiply, Average, Minimum and Maximum, and these operations are premised on ensuring that the amount of feature maps channels of the merge layer to be fused is consistent. By operating the feature maps element by element, it does not increase the number of channels of the feature maps, thus controls the growth of the parameter amount. As shown in The BN layer can effectively alleviate the overfitting phenomenon during the network training process. To explore the actual effect of the BN layer in TAI-SARNET, ablation experiments are performed. The experimental results are demonstrated in The CNN-based model relies on a sufficient number of labeled training samples and achieves good recognition results. However, it is arduous to obtain a large amount of labeled data in the SAR scene, and the recognition results of CNN on small samples datasets of SAR are not ideal. In addition, the SAR target images are full of coherent speckle noise, thus making it difficult for CNN to extract key features and resulting in poor robustness of the model. In view of the above problems, we will explore the robustness of the network proposed in this paper on a small sample dataset of SAR in this section.ResNet50, MobileNet and TAI-SARNET are verified on small sample datasets constructed, and the recognition accuracy of each network is presented in To explore the performance of transferring images of different imaging patterns as source domain knowledge for the domain of SAR targets, we have verified on small samples datasets established in First, the source domain dataset uses the optical dataset ImageNet and pre-trained the model on Inception-v3. Secondly, the target domain dataset uses 10-class MSTAR dataset under SOC, and then uses the parameter-based transfer method to load the underlying weights onto the TAI-SARNET presented in this paper, the recognition results are shown as Transfer1 in To explore the impact of non-optical images knowledge as a source domain on the SAR targets images recognition task, we use 3-class MSTAR dataset  after 360° rotation enhancement as the source domain dataset, and 10-class MSTAR dataset under SOC is used as target domain dataset. First, the TAI-SARNET is used to train on the 3-class MSTAR dataset with full-angle rotation augmentation to obtain a pre-trained model. Then, we load the weights of the pre-trained model on the target task for fine-tuning. As shown in Transfer 2 in In order to further investigate the influence of prior knowledge of the combined optical and non-optical domains as source domain knowledge on SAR target recognition tasks, we have established a complete hybrid dataset based on the NWPU-RESISC45 dataset and 3-class MSTAR dataset rotated at random angles as the source domain dataset, and adopted 10-class MSTAR dataset under SOC is used as the target domain dataset. First, the TAI-SARNET mentioned in this paper is used to train on the hybrid dataset to obtain a pre-trained model. Then, we transfer the weights of the pre-trained model to our target task network and slightly fine tune the initially learned parameters.From Transfer3 in The increasing depth and size of CNNs have brought great challenges to the deployment of deep learning in the terminal equipment, and an efficient SAR targets recognition model needs to consider the number of network parameters and computational complexity while ensuring accuracy. We have tested the representative five classical CNNs and the TAI-SARNET in 10-class MSTAR dataset under SOC, and the experimental results are exhibited in Similarly, we draw comparisons between the method proposed in this paper and other advanced methods applied to 10 classes of SAR target recognition tasks under SOC as well, and the recognition results are shown in In view of the difficult problems in the SAR target recognition tasks, an efficient SAR ATR algorithm with transfer learning is proposed. The Atrous-Inception module is designed based on the atrous convolution and the Inception structure. On the one hand, the module can obtain a rich global receptive field, which helps the network to extract detailed information about limited training data, and improves the robustness on small samples datasets. On the other hand, this module controls the augmentation of the number of network parameters and makes the network lightweight. In addition, the use of the BN strategy speeds up the entire training process and effectively alleviates the gradient divergence and overfitting during the training process. Meanwhile, adopting BN strategy to accelerate the whole training process effectively alleviates the gradient divergence and overfitting phenomenon. Finally, transfer learning strategy is used to explore the recognition performance of transferring prior knowledge in the domains of optics, non-optical, joint optic and non-optical to SAR small samples datasets, and the experimental results have shown that the recognition accuracy is considerably improved. For the 10 classes of SAR targets images recognition tasks under SOC, our method has reached 97.97 percent recognition accuracy, and the experimental results have proved the effectiveness of the method. The small sample data has rich information and is closer to the actual application. It is very meaningful to design an efficient CNN model for small sample data. In the future, we are likely to further delve into the application of related methods of transfer learning and few-shot learning in the domain of SAR targets.


Barely visible impact damage (BVID) is one of the most dangerous types of structural damage in composites, since in most practical cases the application of advanced non-destructive testing (NDT) methods is required to detect and identify it. Due to its character of propagation, there are minor signs of structural damage on a surface, while the internal damage can be broad and complex both in the point of view of fracture mechanisms and resulting geometry of damage. The most common NDT method applied e.g., in aircraft inspections is ultrasonic testing (UT), which enables effective damage detection and localization in various environments. However, the results of such inspections are usually misestimated with respect to the true damage extent, and the quantitative analysis is biased by an error. In order to determine the estimation error a comparative analysis was performed on NDT results obtained for artificially damaged carbon fiber-reinforced composite structures using two UT methods and X-ray computed tomography (CT). The latter method was considered here as the reference one, since it gives the best spatial resolution and estimation accuracy of internal damage among the available NDT methods. Fusing the NDT results for a set of pre-damaged composite structures with various energy values of impact and various types of impactor tips applied for introducing damage, the evaluation of estimation accuracy of UT was possible. The performed analysis allowed for evaluation of relations between UT and X-ray CT NDT results and for proposal of a correcting factor for UT results for BVID in the analyzed composite structures. Composite structures are characterized by high resistance to loads oriented along fiber direction. Unlike metals, composites allow easier forming of non-ruled surfaces with large sizes. This creates a possibility of producing relatively light components with a low number of subcomponents. Another advantage is the fatigue characteristics of composite structures, which is better than in the case of metal structures. However, the experience from the operation of aircraft components made of composite structures pointed to different sources of damage than in the case of metals. In the case of the metal structures, the basic sources of damage are the effect of variable load cycles, whereas in composites the damage is caused mainly by mechanical impacts during maintenance ,2. The cThe most popular method used for a composite inspection is ultrasonics, where a signal can be transmitted in a material and received in a few different ways, but in every case the amplitude or/and time-of-flight are evaluated. Other approaches enabling damage detection on the basis of the emitted sound wave are methods such as: tap-test, mechanical impedance analysis (MIA), resonant method or pitch-catch . In all Basically, the ultrasounds are the acoustic elastic waves with frequencies greater than the audible to human hearing . The ultrasonic testing (UT) is the most often used NDT methods of composite materials ,9,10. ThA radiographic testing (RT) is an NDT method of composites using a short wavelength electromagnetic radiation (X-ray or gamma ray). Variations in material density, thickness, composition, or a presence of defects result in absorbing different amounts of the penetrating radiation by different parts of a tested component. The unabsorbed radiation that passes through the part is recorded on a radiographic film or a photosensor, and the result is shown on the film or electronic radiation detectors . The metAs one can notice, there is not only one chosen successful NDT method used for damage detection in fiber reinforced polymers. The use of each method is associated with limitations and restrictions not only due to the material properties and geometrical constraints but also with failure mode type and location. The awareness of multiple failure mode presence helps with a proper selection of the best tailored NDT solution for a tested case. An option is the use of multiple methods and damage analysis, but such an approach is costly and time consuming. Moreover, the presence of uncertainty in estimation of damage extent is a serious problem, which may limit practical applications of a given NDT method to qualitative analysis only. One of the methods used to overcome such deficiencies is the application of fusion techniques applicable to results of NDT using different methods. The use of data fusion may accelerate the damage characterization as well as increase the diagnostics capabilities. This approach is well known from previous studies available in literature. The authors of  applied Due to the fact that UT results usually present under- or over-estimated damage with respect to true damage , the dam2 in the VARTM resin infusion technology. Then, various scenarios of impact damage were introduced to the specimens using a drop weight testing machine presented in For the presented experiments, specimens made of CFRP structures with the dimensions of 100 × 100 × 2.5 mm (length × width × thickness) were applied. The specimens were produced by the Dexcraft s.c.  using epoxy resin LG 700 manufactured by GRM Systems  reinforced with 10 layers (with the same layup) of a 2 × 2 twill weave Toray T300  carbon fabric of 200 g/cmThe next step was to perform NDT using three methods and acquire the resulting data containing information about the detected damage to be processed in the next steps of this study. Namely, the specimens were scanned using a pulse-echo UT method in the C-Scan mode , were individually processed in order to detect the BVID areas and obtain their reconstructed 3D binary images. For this purpose, previously developed methods by the authors, described in detail in , were apThe next stage was the fusion of the damage regions extracted from the ultrasonic data (B-Scans/C-Scans) with the ones from X-ray CT scans. This was performed by several steps. At first, the damage orientation in all NDT data types had to be unified by their appropriate rotating, which resulted from scanning the specimens in different ways during each of the NDT tests. Then, the ultrasonic data was resized  to have the same resolution as the tomographic scans. After that, a morphological erosion was applied to the ultrasonic data in order to reduce a blurring effect produced as a result of the resizing step. Next, a correction of damage location was introduced by shifting the binary data results to have a common centroid in the planar damage views. Finally, values of the resulting pairs of matrices, that were to be fused, were added after a multiplication with weights different for X-ray CT and UT data, which enabled displaying them as different colors in the visualization of the fusion results. The flowchart for the described algorithm is presented in All the collected NDT results were processed using the above described image processing algorithms. The obtained images of the fused damage areas for an exemplary case (E17.5) are presented in When visually analyzing the obtained images one can observe that the studied BVID is very well detectable using both the applied ultrasonic testing techniques. The identified damage areas are comparable with the reference ones extracted from the X-ray CT scans, but the damage shape is more accurate in C-Scans  detected in a C-Scan/B-Scan, a distance is measured to the point of damage boundary extracted from an X-ray CT scan with the assumption that both points belong to the same curve also passing through the damage centroid. If there is more than one point of the X-ray CT boundary crossing this curve, the outermost one is considered. The example is presented in Mean values of the calculated distances for all the considered cases are presented graphically in Summarizing the above presented results for the investigated damage cases and used NDT techniques, the estimation accuracy of BVID based on ultrasonic B-Scans turned out to be very high when considered only the mean distances between the detected damage contours’ points to the reference data (X-ray CT scans), which can be related also to the possibility of calculation of a proper surface area of damage. However, taking into account also the shape of detected damage, it can be stated that C-Scans brought more accurate information, but with the damage overestimation of about 2.27 mm in each direction. These damage size differences can be explained by many factors influencing on the measurement uncertainty using ultrasonic testing techniques, such as an applied transducer frequency and a beam diameter, which was described in detail in a previous authors’ work .In further experiments based on the examined composite structures and considered BVID scenarios, the authors plan to apply a correcting factor of 2.27 mm for ultrasonic C-Scans , which will give more reliable information about real damage extent while maintaining its proper shape. Using such corrected C-Scans instead of the obtained B-Scans for the planned purposes of damage modeling and estimating the residual strength of the composite structures will provide better results due to the good shape reflection, which may be important in the context of the direction of damage propagation.In the following study, the authors presented the results of fusion of non-destructive testing results of CFRP structures with BVID using two modes of UT, B- and C Scans, as well as X-ray CT used here as the reference method. The proposed fusion approach allowed to characterize the resulting damage after low-velocity impact loading taking into consideration parameters of this loading, estimate the detectability of impact damage using both UT methods basing on fusion of UT and X-ray CT results, and finally to evaluate misestimation of B- and C-Scans, which are commonly used in inspections of composite structures, in particular, aircraft structures, with respect to X-ray CT scans, which provide much higher resolution and sensitivity to damage than the mentioned UT methods. The importance of determination of this misestimation results from two factors of the practical inspection procedures: first, the X-ray CT method is limited by the size of tested objects and the tests can be performed in laboratory conditions only; and second, the cost of testing using UT is significantly lower than when using the X-ray CT method. The results of the performed tests and further image processing of the obtained results allowed for determination of the correcting factor for UT testing, which allows to represent the resulting BVID in the tested CFRP specimens within this method in a more realistic manner. It should be highlighted that, due to the necessity of individual selection of UT techniques and parameters for each tested composite structure, such a correcting factor should be calculated using the presented method individually for different types and dimensions of a composite with BVID being evaluated. This also applies to the situation of investigating different damage scenarios.


Fibre reinforced plastics have tailorable and superior mechanical characteristics compared to metals and can be used to construct relevant components such as primary crash structures for automobiles. However, the absence of standardized methodologies to predict component level damage has led to their underutilization as compared to their metallic counterparts, which are used extensively to manufacture primary crash structures. This paper presents a methodology that uses crashworthiness results from in-plane impact tests, conducted on carbon-fibre reinforced epoxy flat plates, to tune the related material card in Radioss using two different parametric identification techniques: global and adaptive response search methods. The resulting virtual material model was then successfully validated by comparing the crushing behavior with results obtained from experiments that were conducted by impacting a Formula SAE (Society of Automotive Engineers) crash box. Use of automated identification techniques significantly reduces the development time of composite crash structures, whilst the predictive capability reduces the need for component level tests, thereby making the development process more efficient, automated and economical, thereby reducing the cost of development using composite materials. This in turn promotes the development of vehicles that meet safety standards with lower mass and noxious gas emissions. In 2012, the European Union decided to reduce vehicular average emissions by 27% from 2015 to 2021 and, using 2021 as a baseline, further reduce them by 15% and 37.5% by 2025 and 2030, respectively ,5. CompoThe improved specific energy absorption capabilities of structures made from composite materials, as compared to those made from metals, have been well-documented ,10,11,12The present study summarizes a methodology that can be used to predict component level damage behavior using numerical models that have been tuned using results of axially impacted flat composite plates, with calibration of the same conducted using parametric identification, all of which was done using the HyperWorks software package. HyperMesh was used for pre-processing, HyperView and HyperGraph for post-processing, HyperStudy for parametric identification and Radioss as a solver. Macro and meso-scale approaches have been considered to develop procedures suitable for industrial structure design. In order to test the flat plates, the methodology takes advantage of an anti-buckling fixture developed by Politecnico di Torino and Instron . Flat coThe paper presents the experimental and numerical methodology and the automated identification setup in For the first level of the BBA, standard ASTM tests were performed for tensile (D3039), compression (D3410), and flexural (D0790) characterization of the CFRP laminates made of GG630T-37  2 × 2-twThen, moving upward in the BBA, in-plane impact tests were executed, using Instron 9450 drop tower testing machine  with 1800 J of impact energy capacity, on saw-tooth triggered flat specimens to evaluate the energy absorption capabilities in the element level. Since flat elements tend to buckle under in-plane forces, a new anti-buckling fixture was designed and manufactured for these tests . Figure For the final step of the BBA, six crash attenuators were manufactured to analyze their responses under different impact loading conditions. The attenuators were composed of three sections with different wall thicknesses, which were intended to work as trigger mechanisms, ensuring that steady crushing started from the top of the specimens. At first, two quasi-static tests at 10 mm/min were performed using ZwickRoell electromechanical testing machine  with 100 kN of load capacity. Then, four different dynamic tests with 300 kg impact mass were carried out using the drop weight testing facility at Picchio Spa . The flat-plate model was composed of a composite material specimen impacted by a falling steel plate, while the composite plate sat on a steel base and was held in place by four anti-buckling supports . MetalliComposite material, CFRP in this case, was modelled using the CRASURV formulation of material law 25 in Radioss. CRASURV formulation is a modified form of the Tsai-Wu criteria that was developed as a part of the IMT 3 Brite Euram EU program and was validated on various composite materials and the underbelly and airframe of the A320 aircraft, and has since been used extensively in the aerospace industry ,31. CRASProperty type 11, a property available in Radioss for a composite shell modelling, was used to define the layup of the CFRP laminate. It allowed for modelling the element type, thickness, layer position and orthotropic direction of each ply. Property type 1, i.e., simple shell, was used to model metallic components. Fully integrated Batoz shell elements were used for all the models. Contact modelling was defined using the node-to-element (Type 7) and surface-to-surface (Type 24) contacts. Respective contact models used are reported in Anti-buckling cylindrical supports were modelled using a 1 mm mesh to capture the column curvature, while base and impactor plates were modelled with a 5 mm mesh size. All three components were modelled as rigid bodies. Boundary conditions applied to the base and anti-buckling supports resulted in zero degrees of freedom (DoFs) for these components, whilst for the impactor a single DoF was set, allowing it to translate freely in the y-direction  only. Composite specimen was modelled with 4 node quadrilateral (quad) shells, with a 4 mm mesh size, which was considered the best trade-off between accuracy and efficiency, based on previous published studies conducted on crashworthiness of composite structures ,36,37. AThe plate trigger was machined as a sawtooth for experiments as shown in A similar modelling approach was followed for modelling the impact attenuator. The model consisted of the CFRP attenuator placed between a steel steady base and steel falling plate. Metallic components were modelled with a 10 mm mesh size, whilst the attenuator was modelled with a 4 mm mesh size to ensure continuity from the flat-plate methodology as impact behavior is mesh dependent. Owing to the geometry of the attenuator, it was composed of both quad and triangular elements. The attenuator was divided into three sections: top section made of two plies 46 mm long, middle section made of three plies 70 mm long and bottom section made of four plies 100 mm long, wherein each ply was 0.75 mm thick.The material card was unchanged from the flat-plate model, to ensure continuity regarding the material properties. Property card was modified to account for the different ply configurations. Base and top plates were made rigid bodies to ensure all elements moved synchronously. Boundary conditions applied to the base ensured no DoFs, whilst those applied to the falling plate allowed it to translate in the x-direction  only. The final model consisted of approximately 14,000 elements and 64,500 DoFs, while the attenuator was composed of 62 tria and 10,300 quad elements that allowed 62,700 DoFs. The model is shown in As delamination modelling, which is a major failure mode in impact cases ,38,39, wThe composite flat plate was composed of four plates made of shell elements, each of which represented a ply 0.64 mm thick. The three solid cohesive element layers, modelled between the four plies, were modelled as 0.64 mm thick in order to fill the gap between the shells. The final model was composed of 18,100 element and 25,000 DoFs, of which the composite specimen was composed of 6500 elements and 24,300 DoFs. Law 59 Connect was used to model the adhesive layer, which constituted the cohesive elements. This law allowed elastic and inelastic behavior to be modelled in normal and shear directions . Table 5The adopted failure criterion was strain-based. It was preferred over the energy-based, as an energy-based criterion requires data from double cantilever beam (DCB) and end-notched flexure (ENF) tests, which were not available ,43,44. DA similar approach was followed for the impact attenuator: the top section was composed of a layer of cohesive elements bounded by two layers of shell elements, the middle section was composed of two layers of cohesive elements bounded by three layers of shell elements, and the bottom section was composed of three layers of cohesive elements bounded by four layers of shell elements. The only difference between the flat plate and attenuator modelling using cohesive elements was the thickness of the plies and the cohesive elements. The thickness used for the impact attenuator was 0.75 mm. Each layer of cohesive elements represented adhesion between the plies and each layer of shell elements represented a ply. The final model was made of 62,000 elements and 211,000 DoFs, of which the attenuator was made of 58,000 elements and 209,000 DoFs. HyperStudy was used for identification runs due to the ease of its interface with Radioss. The objective of the identification was to set a more robust procedure as compared to the trial-and-error approach used in previous published studies in order to tune the material cards, thereby reducing the setup time. Use of identification for calibration also eliminated the subjectivity involved in the trial-and-error approach and the possibility that the calibrated values thus obtained were not the global minimum. The general setup of the identification involved defining the input variables and their respective bounds, running a system bounds check to ensure that the analysis did not give any errors when the lower and upper bounds were used, defining and evaluating an output response, setting an objective for the output response, choosing the optimization approach to follow, running the identification and post-processing, in that order.The numerical parameters that could be optimized were:3, based on a value obtained from the literature [3, which was the area under the shear stress–strain curve.Damaging work was the limit value in the element deletion criteria while residual stresses were used to define softening behavior. As for the particular type of material considered in this study, properties were assumed to be the same in 1 and 2 directions; therefore, strength  and the terature , while tOf the various optimization algorithms available in HyperStudy, global response search method (GRSM) and adaptive response search method (ARSM) were selected due to their suitability to the problem: deterministic single objective optimization with multiple variables and few constraints. The response surface for both GRSM and ARSM were updated after each iteration. It is known from the literature that GRSM works better when a global optimum is desired and there are a number of design variables. In addition, GRSM is known to be more robust as compared to ARSM, whereas the latter is more efficient ,47. BaseTwo response functions were tested: the integral of absolute difference between the experimental and the simulation values of the crush force as shown in Equation (6) and the integral of squared difference between the experimental and the simulation values of the crush force as shown in Equation (7). Although both the methods produced a material card that lead to close correlation between the experimental and numerical average crushing force, the displacement stroke was better modelled with the absolute difference approach as compared to the squared difference approach. The absolute difference approach led to a final difference of 7% while the squared difference approach led to a final difference of 25% as compared to the experimental stroke displacement. Hence, the absolute difference approach was preferred and used for subsequent calculations.3 as the upper bound used for identification with shell element model only were too low leading to the optimized value arriving at the upper bound.The identification setup for flat plate with cohesive elements was similar to that of the runs with only shell except that the upper bound for Tests on triggered flat specimens were used to calculate the specific energy absorption (SEA) of the material. To calculate SEA, the following formula was used:For the impact attenuator, two quasi-static tests were performed for better understanding of the material and demonstrator response under crush forces. Finally, four dynamic impact tests were performed on the remaining four impact attenuators. In order to study the effect of the contact stiffness between components during contact modelling, all contacts were initially set to the minimum stiffness value of 1 kN/mm. Seven combinations of contact stiffnesses were studied as mentioned in Of the three cases considered in the identification process, E led to multiple buckling in the entire length of the specimen possibly because the contacts became too stiff. Cases F and G, correctly, developed fronds only at the plate bottom, but for case F frond formation extended above the unsupported height while for case G it was restricted only below the unsupported height. Therefore, contact stiffness calculation based on Young’s modulus and thickness was activated for the contacts between the impactor, base and specimen and the specimen and the supports while a stiffness of 1kN/mm was used for the interface between the elements of the specimen. Case G was the final identification run on the flat-plate and Fronding, as observed in experiments, could be seen clearly, when cohesive elements were introduced into the model as shown in Results of the optimized iterations derived from final identification runs conducted on flat plates using both the macro and meso-scale approach at 550 J impact energy and 7 m/s impact velocity are shown in Upon visual inspection of the experimental tests, it was observed that two plies fronded to one side, while one fronded to the other and one of the middle plies was crushed. A similar behavior was observed in the simulation as evidenced in Identifications were conducted with GRSM algorithm and absolute difference was used as the response function. The identification consisted of 50 iterations leading to a total run time of about 250–300 min, as each iteration was 5 min long for macro-scale approach. Run time with the meso-scale approach was 65 min for each iteration resulting in a total run time for the identification of about 60 h. Identification simulations were able to converge to a material card that could model the crushing force after initial part that was influenced (disturbed by) the trigger with reasonable accuracy see . Stroke Using the optimized material card, simulations were run on the impact attenuator to validate the methodology and the results of the same are shown in An initial peak, also seen in flat plate tests, was observed in the impact attenuator numerical simulation for both models, with and without cohesive elements. The peak in the flat plate tests was attributable to the difference in modelling the trigger as compared to its geometry in reality, but this was not the case with the impact attenuator. No trigger was embedded into the geometry of the impact attenuator as its increasing area and difference in the thicknesses of the three sections act as a trigger. The same was true for the numerical model. However, embedding a trigger to initiate failure could have reduced the peak in the numerical model. The high peak was partially, also, due to the numerical model of the base that was classified as a rigid body, which does not permit any DoFs ultimately making the model stiffer than it actually should be when compared with the physical setup wherein the base flexed by about 6–8 mm upon impact. Modelling the base with all DoFs without the rigid body, would have caused it to flex as it did during experimental testing, thereby reducing the initial peak and improving the correlation with experimental results. Subsequently, upon adding this displacement caused by the flexion of the base in the experiments to the final numerical displacement of the model without and with cohesive elements, the difference in the final displacement reduces to less than 5% and around 5%, respectively, for the two different adopted models. Displacement of model with cohesive elements was lower when compared to the model without cohesive elements because of higher forces in the middle section. Average crushing force for the three sections was predicted accurately for the model without cohesive elements. The same was not true for the model with cohesive elements, wherein the force was predicted accurately for the first sections, but was overestimated by about 10% for the middle section of the attenuator. This overestimation was possibly due to the higher value for the parameter Time step for the model with cohesive elements dropped by five orders of magnitude, due to compression of cohesive elements, from its initial time step, which could have caused the model to take an excess of 10,000 h to run. Therefore, a command was added to the model with shell and cohesive elements that disallowed the time step to drop below 66% of the initial time step. The command used has no effect on the results when used for models with non-hyperelastic materials and allowable time step less than the initial time step .In this study, a methodology that is able to predict the impact response of composite components was presented. The methodology involved conducting in-plane impact tests on flat composite plates using a newly developed fixture that works seamlessly with Instron drop towers. The results of the experimental investigation on these flat plates were used to calibrate three numerical parameters of the material card in Radioss using an automated parametric identification procedure. The material card obtained was able to predict the impact response of a Formula SAE crash box made of the same material with reasonable accuracy for the crushing force values and history and for damage visualization. Stroke displacement was predicted within 5% of the experimental values, thereby validating the methodology. For the in-plane impact test, contact was best modelled when the contact stiffness applied to the contact between the elements of the composite specimen (self-contact) was set the minimum stiffness of 1 kN/mm. The contact stiffnesses for the rest of the contacts was calculated based on the Young’s modulus and thickness for the respective components. Comparison of the results obtained with the two considered optimization algorithms, GRSM and ARSM, showed that GRSM is more robust, and it is recommended for research purposes, whilst ARSM is more efficient, and can be used in the industry applications. The integral of the absolute difference between the numerical and experimental force values over the displacement was a better response objective as compared to squared difference, as the application of the former produces final stroke displacement values closer to those obtained experimentally. Identifications were able to obtain a material card that predicted both the force and stroke displacement simulated values within 5% of the experimental results. Although the macro model consisting only of shell elements was not able to capture delamination behavior, it was able to predict force and displacement of component upon impact with < 5% error, whilst simulating macro damage visualization with reasonable accuracy. The meso-scale model consisting of shell and cohesive elements captured delamination behavior accurately, but overestimated the force in the middle section by an acceptable 10%, in addition to taking significantly longer time to run. Therefore, a macro-scale approach is suggested for industrial applications. Overestimation of force due to non-deletion of elements in the meso-scale approach as a consequence of the high values obtained for the primary element deletion criterion could be resolved by using additional failure criteria. The developed methodology should help increased integration of composite materials into primary crash structures due to reduced expenditure on expensive experimental tests as only material characterization and crashworthiness tests using the anti-buckling fixture will need to be conducted, whilst component and parts of full-scale testing could be replaced by numerical analysis. This would also result in relevant timesaving, thereby leading to reduced costs for development of such structures. The adoption of composite material structures should ultimately lead to lower emissions of noxious gases for vehicles, as they would be significantly lighter taking advantage of the higher SEA of composite materials as compared to metals.


Pedestrian detection is an important task in many intelligent systems, particularly driver assistance systems. Recent studies on pedestrian detection in infrared (IR) imagery have employed data-driven approaches. However, two problems in deep learning-based detection are the implicit performance and time-consuming training. In this paper, a novel channel expansion technique based on feature fusion is proposed to enhance the IR imagery and accelerate the training process. Besides, a novel background suppression method is proposed to stimulate the attention principle of human vision and shrink the region of detection. A precise fusion algorithm is designed to combine the information from different visual saliency maps in order to reduce the effect of truncation and miss detection. Four different experiments are performed from various perspectives in order to gauge the efficiency of our approach. The experimental results show that the Mean Average Precisions (mAPs) of four different datasets have been increased by 5.22% on average. The results prove that background suppression and suitable feature expansion will accelerate the training process and enhance the performance of IR image-based deep learning models. Pedestrian detection is a vital research topic in the field of computer vision, one of significant theoretical interest, and with various applications. Different types of sensing devices can be used to capture information from diverse dimensions. Of these, the specialized use of infrared imagery for pedestrian detection in images is of particular interest, since they contain different information from the visible light images. This information, especially those from thermal radiation, is available to be employed to detect targets with higher temperatures. The infrared radiation of some wave band can penetrate through the cloud and mist. Moreover, information from infrared imagery can be employed both day and night. Therefore, such types of detection can be used not only in intelligent surveillance systems, but also in the advanced driver assistance systems (ADAS) for greater safety for humans and vehicles.In the infrared (IR) spectrum approach, the IR cameras receive radiation emitted from the scene in the daytime and nighttime conditions ,2, and tCurrent research on human detection can be broadly classified into two categories: Expert-driven and data-driven learning approaches .In the expert-driven learning approaches, feature extraction and classification are two necessary steps. Feature extraction of the human body in the image is carefully designed in order to transform the raw image into a feature map, which can be recognized by the classifiers. The algorithm of pattern extraction is designed or adjusted by experts, according to the discipline of image processing or the features of the object itself. Many efforts have been made to develop shape features that are not sensitive to brightness, orientation, or scale. However, the generalization capacity of expert-driven learning approaches is often very limited, and some of them are designed to solve specific problems . For insThe data-driven learning approach, especially deep learning, has attracted a great deal of interest in recent years, not only for its outstanding performance in both data classification and object detection, but also for its ability to learn features and patterns from raw images without human support. Convolutional neural networks (CNNs) were first designed according to the principles of information processing and communication in a biological nervous system . AlexNetThis is the first research on IR pedestrian detection using an IR image enhancement unit to extract the artificial features as the input of the CNN, and using a feature optimization network to output a further optimized feature map.In this research, experiments were carefully designed, and they proved the assumption that appropriate expert-driven features can help with the extraction of CNN features and accelerate the training process of the model.The detection performance has been improved by the model proposed in this research, compared with the baseline methods and original region proposal networks, which was proved by the experiments.A new saliency fusion method was designed in this research to suppress the background. This fusion method proved that background suppression could improve the performance of pedestrian detection by reducing the negatives, according to the experiments.Therefore, in this research, an assumption has been given that appropriate expert-driven features can help with the extraction of CNN features and accelerate the training process of the model. In this work, an IR image channel expansion enhancement approach is proposed to address the problems described above. The domain expertise of the expert-driven approach is applied to pre-extract the features of the IR imagery, thereby speeding up the training process of the data-driven detection training process. At the same time, we employed several visual saliency methods to shrink the area that remains to be searched, in order to simulate how the human eye pays attention to an object. Current visual saliency methods cannot handle the many types of IR images, because the IR sensors are all different. This issue is mitigated in this study, and a fusion method is proposed to solve this problem. Our research is novel compared to previous works in the following four ways:The remainder of this paper is organized into four sections. This study focuses on enhancing through channel expansion and saliency methods the performance of CNN-detection on IR imagery. Therefore, we briefly review the related work in terms of features in expert-driven approaches and visual saliency methods.Many researchers have put great effort into developing the extraction of shape features, as the shape of a human is a critical feature for human classification, particularly in infrared images. The histogram of oriented gradients (HOG) and its variants are among the most frequently used techniques employed in machine learning. The usage of HOG did not become widespread until 2005, when Dalel and Triggs put forth the importance of the gradient feature in accurately representing the appearance and shape of an object. Therefore, HOG was first designed for classifiers, for example, support vector machine (SVM), to recognize the class of target . HoweverLocal binary pattern (LBP) is a local textural feature with substantial rotation invariability and gray-level invariability. It was first put forth by Ojala, Pietikäinen, and Harwood in 1994 in order to extract texture in small parts of an image. Ojala et al., employed a circle mask of operator for sampling in order to deal with various sizes and frequencies . To handOver the last two decades, many other interesting approaches have been proposed for detection in various applications. These include the edgelet features proposed by Wu and Nevatia , the scaBecause the aforementioned approaches are based on an empirical model, they may change significantly with the season, weather, and background, especially when it comes to IR imagery conditions.In recent years, visual saliency methods have been developed to extract the region of interest. This kind of approach, takes raw imagery as input and calculates the saliency map of the image via stimulating the visual attention mechanism—which means that the model evaluates each pixel and generates a score for it. This score is usually in proportion to the possibility of the pixel being a part of the object.One of the earliest saliency models, proposed by Itti et al., is an implementation of earlier general computational frameworks and psychological theories of bottom-up attention-based on center-surround mechanisms . In the Another bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), was proposed by Harel, Koch, and Perona . It consA technique to highlight sparse salient regions was proposed by Hou et al., which is referred to as the image signature . This moPrevious studies show that visual saliency methods are suitable for grayscale images, especially IR images, without color distribution . MoreoveThe specialization of detection networks to classification networks begins with the region proposal method. The selective search was first put forth to provide several candidates for classifiers to recognize, by cutting the image into different areas with the same color, texture, and gradient features ,38. In 2The present study combines expert-driven and data-driven approaches to provide an economical, effective solution for IR pedestrian detection. Gradient features and MBLBP operator are used to generalize the IR data from different sensors, while expanding the channels of the images. Visual saliency models are used to restrain the background and reduce the false positives by shrinking the size of the candidate regions. Fast R-CNN and Faster R-CNN detection models are employed to verify the rationality of our model, and through experiments, demonstrate its accuracy and its economy in terms of computing resources.In this section, an IR imagery enhancement approach based on feature expansion and background suppression for infrared pedestrian detection is presented. The proposed approach consists of two main procedures: The feature channel expansion and background suppression. The input of this system is raw IR imagery of scenes containing pedestrians and background. The system generates as output both the position and probability of the pedestrians predicted in the images.Because the size of the input image varies depending on the type of sensor, the input image is first normalized on size and transformed into a fixed size (height of 1000 pixels and width of 1600 pixels). This size normalization is used to unify the sizes of images from different datasets. Moreover, there will be different performance if we apply the same convolution kernel or filtering kernel on images with different sizes. Therefore, size normalization can improve the robustness of the system. Feature expansion is designed to generate artificial feature maps. The model expands the channels of IR images with feature maps, because this research makes the assumption that appropriate expert-driven features can help with the extraction of the CNN features and accelerate the training process of the model. The selected features will be introduced in As is shown in It is assumed that appropriate expert-driven features can help with the extraction of the CNN features and accelerate the training process of the model. In order to verify the assumption, two feature descriptors are employed to extract information from the IR image. Expert-driven approaches often use shape information  and texture information  . In our The algorithm of feature channels expansion is shown in The image gradient is usually defined as the directional change in the gray level. Suppose that In the digital image, the differential can be replaced by difference.The gradient magnitude can be computed as:The intensity normalization of the gradient can be described as:The original LBP operator labels the pixels of an image by thresholding the l number . Multi-sl number .The binary sequence can be transformed into a unique LBP number that represents the local image texture , which cIn the MB-LBP, the comparison is between average gray-values of sub-regions, instead of between single pixels in one sub-region. As shown in The background suppression component of our system combines two types of saliency algorithms to suppress the background. These two saliency algorithms generate the GBVS map and Itti-Koch map to estimate the possibility that a particular pixel or region belongs to an object. Since human beings are distinct in IR images, due to their high intensity, saliency approaches are more suitable for IR imagery.Algorithm 1 illustrates the proposed background suppression according to GBVS and Itti-Koch Map. These two maps are first normalized, because the range of the two maps is different. Next, we compare the two maps in each sub-region, and pick the larger one of the simple sub-region as the saliency value. The intensity distribution of the original image is then modulated by the value, and the background suppressed image is generated.As shown in Algorithm 1 Background Suppression according to GBVS and Itti-Koch MapInput: an original infrared image I0Output: a background-suppressed imageI ← standardize Image (I0)1: the standardized image   //Both histogram equalization (HE) and size normalizationStep 1: GBVS map and Itti-Koch map generation:  //I)2: GBVS map ← GBVS(I)3: Itti-Koch map ← Itti-Koch, South China University of Technology far infrared ray pedestrian dataset (SCUT), CVC14 day-night pedestrian sequence dataset (CVC14), and Changchun University of Science and Technology far infrared ray (CUSTFIR) pedestrian dataset.The LSI dataset was obtained from a vehicle driven in outdoor urban scenarios. Images were acquired with an Indigo Omega Imager, with a resolution of 164 × 129 pixels. The training set contained 10,208 positives and 43,390 negatives, while the test set contained 5944 positives and 22,050 negatives .SCUT is a large far-infrared pedestrian detection dataset . It consThe CVC14 day-night pedestrian sequence dataset was obtained on a moving platform on the street in both daytime and nighttime settings . OverallCUSTFIR is a dataset obtained by our team with an automotive IR camera developed by iRay Technology, in Shanghai, China. The IR camera has a resolution of 352 × 288 and a sampling frequency of 25 Hz. This dataset contains both daytime and nighttime sequences. There are 2836 images with 6408 pedestrians included. All images are recorded with a one out of 10 sampling. The ground truths of this dataset are annotated manually by four different volunteers. The dataset contains two different scenes with irregular background, such as trees, cars, buildings, and sky. The two scenes are on different roads of Changchun University of Science and Technology. The datasets contain sequences of both winter and summer, and it contains sequences of both day and night. Some examples of CUSTFIR pedestrian dataset are shown in The ground truths of each pedestrian are the average of annotations given by the four volunteers. When the ground truths are manually annotated, we have defined a threshold for the minimum height of the pedestrians. That is, when we did the experiments, we ignored the ground truths whose bounding box’s height is smaller than 30 pixels.All scenarios of the four datasets are realistic scenarios. The basic information on the infrared pedestrian datasets used in the experiments is shown in the Each experiment consisted of image enhancement and deep learning with RPNs. The former was conducted on the MATLAB platform on a desktop PC with a 3.1 GHz quad-core Intel Core i5 processor and 8 GB RAM. The latter was conducted on the Torch platform on a professional image-processing PC with a GeForceRTX2080Ti and 11 GB RAM.A true negative is a case where the background area was recognized as a background region.A true positive is a case where the human area was correctly recognized as a human region.A false negative is a case where the human area was recognized as a background region.A false positive is a case where the background area was recognized as a human region.This paper uses quantitative evaluation methods for a classical detection task to gauge the performance of our approach. According to Pascal criteria, the detection results can be classified into four situations: True positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). For a single class detection task, these four situations can be defined as follows:The relationship among four situations can be represented by the Venn diagram in In addition, based on the aforementioned TP, TN, FP, and FN, we used the following four criteria to measure accuracy :(9)PreciThe Precision–Recall Curves (PR Curves) are based on the aforementioned Precision and Recall. In the final step of the detection, 11 thresholds are set to generate models of different sensitivity. If the threshold was set too high, there would be more FNs and less TPs. If the threshold was set too low, there would be more FPs and less TNs. Therefore, a value, referred to as Mean Average Precision (mAP), should be calculated for each model.In this experiment, images were presented at different steps of the process, in order to show the visual results in an intuitive way. The next experiment used the LSIFIR dataset, CVC-14 database, SCUT database, and CUSTFIR dataset to quantitatively estimate the performance difference between the model with channel expansion and background suppression (our work) and the model with the only Faster R-CNN. The model with only the channel expansion approach was also tested in order to show the effect of background suppression. For the LSIFIR dataset, we ran our approach and Faster R-CNN five times to obtain the mean and standard deviation. The Precision, Recall, Accuracy, and F-measure among the four datasets are shown in The mAPs were calculated from the area under the PR Curve in order to evaluate the general performance of all the approaches, which is showed in In this experiment, mAP is considered as the function of the amount of training data. Two curves are drawn in order to show the difference in data requirements among the approaches, which takes the mAP as the ordinate against the epoch as the abscissa.In this research, an enhancement method for pedestrian detection with channel expansion and background suppression approaches has been studied in an IR imagery environment. The magnitude of gradient and MB-LBP are employed to extract further information from the original image, and two visual saliency methods are used to shrink the area that remains to be detected. In addition, a cautious combination algorithm has been designed to avoid the truncation and miss detection of pedestrians. The experiments demonstrate that the performance of our work is better than the models with only data-driven methods. The results show that the proposed feature expansion and background suppression can effectively extract the features and reduce the region of searching. The mAPs of the model have been improved by 2.98%, 8.52%, 6.18%, and 3.20%, respectively, on four datasets with different scenarios. In future work, we will adopt C++ to realize the algorithm and testing the time consumption. In addition, most of the parameters in this work have been optimized for this particular task. The more data collected in various scenarios require more optimizing investigation work that could focus on a type of model, in which the parameters could be self-adapted and trained.