The accurate identification and real-time detection of obstacles have been considered the premise to ensure the safe operation of coal mine driverless electric locomotives. The harsh coal mine roadway environment leads to low detection accuracy of obstacles based on traditional detection methods such as LiDAR and machine learning, and these traditional obstacle detection methods lead to slower detection speeds due to excessive computational reasoning. To address the above-mentioned problems, we propose a deep learning-based ODEL-YOLOv5s detection model based on the conventional YOLOv5s. In this work, several data augmentation methods are introduced to increase the diversity of obstacle features in the dataset images. An attention mechanism is introduced to the neck of the model to improve the focus of the model on obstacle features. The three-scale prediction of the model is increased to a four-scale prediction to improve the detection ability of the model for small obstacles. We also optimize the localization loss function and non-maximum suppression method of the model to improve the regression accuracy and reduce the redundancy of the prediction boxes. The experimental results show that the mean average precision (mAP) of the proposed ODEL-YOLOv5s model is increased from 95.2 to 98.9% compared to the conventional YOLOv5s, the average precision of small obstacle rock is increased from 89.2 to 97.9%, the detection speed of the model is 60.2 FPS, and it has better detection performance compared with other detection models, which can provide technical support for obstacle identification and real-time detection of coal mine driverless electric locomotives. The driverless technology of coal mine rail electric locomotives has aroused wide attention as coal mine intelligence leaps forward2. The driverless technology of electric locomotives is capable of avoiding electric locomotive collisions, derailments, and other accidents caused by the poor coal mine roadway environment and the uncertainty of manual driving. Moreover, this technology is capable of achieving the purpose of reducing personnel and increasing efficiency, thus having significant economic benefits.The coal mine rail electric locomotive, one of the major pieces of equipment in coal mine production, undertakes the transportation tasks , which is a vital link to determining the efficient production of coal mines4. Narrow coal mine roadway space and uneven roadway walls will make the data collected by LiDAR have more noise, thus resulting in an unsatisfactory detection effect. In contrast, image processing techniques-based deep learning target detection algorithms automatically learn image features based on convolutional neural networks (CNN), which have been gradually used in extensive target detection tasks in coal mines for their higher detection accuracy and faster detection speed. Xu et al. proposed a CAP-YOLO algorithm for intelligent monitoring of pedestrians in coal mines through the lightweight improvement of the YOLOv3 algorithm, thus effectively increasing the reasoning speed of the algorithm and detecting pedestrians in real-time5. Liu et al. applied the optimized YOLOv4 model to the intelligent separation task of coal and gangue, thus increasing the recognition accuracy and speed6. Cai et al. proposed a deep learning-based detection algorithm for granularity detection and identification of mine-dumped materials, aggregates, and clays with good detection results7. Li et al. proposed a TMRC-SSD algorithm to identify and detect gangue, bolt, stick, and other impurities mixed in raw coal, thus ensuring the safe production and mining efficiency of coal8. It can be seen that the above scholars have improved the deep learning target detection algorithms to improve the detection accuracy and speed of the targets in different scenarios to varying degrees and realize accurate real-time detection. Therefore, applying deep learning detection algorithms to the obstacle detection of driverless electric locomotives will be helpful in improving the detection accuracy of obstacles and guaranteeing real-time detection, which is of great significance for improving the safe operation of driverless electric locomotives.The driverless technology of electric locomotives is essentially designed to identify and detect obstacles that may appear in front of the locomotive for autonomous obstacle avoidance. The existing research on the driverless technology of electric locomotives in coal mines worldwide remains in the preliminary stage. The obstacle detection technology in complex coal mine roadway environments is not mature. A few domestic coal mines realize the obstacle detection of electric locomotives based on LiDAR technology and, at the same time, realize electric locomotives that are driverless with remote monitoring and an intelligent dispatching system. However, the processing process based on LiDAR detection is complicated and vulnerable to environmental effects9, MASK-RCNN10, etc., and one-stage detection algorithms include Single Shot Detector (SSD)11 and You Only Look Once (YOLO) series15, and so on. Two-stage detection algorithms have a deep network structure and a large amount of computation, resulting in slower detection speeds for these algorithms, while one-stage algorithms have a relatively simple network structure and fast detection speeds, making them suitable for scenarios that require high detection speeds. Since the two-stage detection algorithm cannot meet real-time detection18, this study adopts YOLOv5s, which has a faster detection speed, as the detection model. The model is optimized according to the characteristics of insufficient light, partial occlusion, and a large number of small obstacles in the coal mine roadway to improve the detection accuracy of the model, and the ODEL-YOLOv5s model is proposed. The main contributions of this study are as follows:Mixup, Cutmix, Cutout, and Mosaic data augmentation methods are introduced to expand the dataset and improve the diversity of obstacle features.The Convolutional Block Attention Module (CBAM) is introduced to the neck of the model to focus on interesting features and increase the receptive field of the model.A small target prediction layer is added to the model to enhance the detection ability of the model for small obstacles.The localization loss function and non-maximum suppression method of the model are optimized to improve the regression and reduce the redundancy of the prediction boxes.Deep learning target detection algorithms are classified into two types (including two-stage and one-stage), among which two-stage algorithms include FAST-RCNN19. Its core idea is to take the whole image as the input of the model, extract the input image features on CNN multi-scale, further fuse the extracted features, and then send the image features to the prediction layer for bounding box regression and category probability prediction. The YOLO algorithm can divide the input image evenly into N × N squares. B bounding boxes, and the confidence scores of the above bounding boxes will be predicted in the respective square. If the center of an obstacle in the image is located in a square, this square is ultimately responsible for the prediction of such an obstacle20.The YOLO algorithm is capable of simplifying the problem of target detection into a regression problem21 and Path Aggregation Network (PANet)22. The SPP module is capable of extracting fixed-size feature vectors from different-scale feature maps to solve the problem of different sizes of input images, thus effectively increasing the receptive field of the model. Under the PANet module, path expansion and aggregation are conducted to enhance the bottom-up path, thus making bottom-level information easier to propagate and shortening the information path between low-level and high-level features. The YOLOv5 model draws lessons from Cross Stage Partial Network (CSPNet)23 while designing CSP1-n and CSP2-n modules in accordance with whether there are residual components. The CSP1-n is applied to the Backbone of the model, and the CSP2-n is applied to the Neck of the model. In the Head part, the YOLOv3 detection head is employed by the YOLOv5 model for multi-scale prediction.There are four versions of the YOLOv5 model, i.e., YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. To be specific, the YOLOv5s model is the lightest version in the YOLOv5 series; the other versions are deeper and wider based on the YOLOv5s model. As depicted in Fig. The loss function is the difference between the predicted value and the true value of the model. The smaller the value of the loss function, the higher the prediction accuracy of the model will be and the better the classification effect will be. The loss of the YOLOv5 model comprises three parts, namely classification loss, confidence loss, and localization loss.IoU) is usually employed as the localization loss of the model in the field of target detection, i.e., the bounding box regression loss. As depicted in Fig. The IoU loss function (LGIoU) serves as the bounding box regression loss in the conventional YOLOv5 model. The LGIoU considers the minimum closure rectangular area of the Pre and the GT based on the LIoU to avoid the problem that the Pre and the GT have no overlapping area, thus making the value of the IoU approach zero and causing the model training to fail to converge. The LGIoU calculation equation is presented as follows, where Ac denotes the minimum closure rectangular area of the Pre and the GT, and U expresses the union area of the Pre and the GT.The GIoU loss function The conventional YOLOv5s model adopts three scales of detection layers for target perdition, and for the input 608 × 608 image, the feature map sizes on the three detection layers are 19 × 19, 38 × 38, and 76 × 76. The downsampling multiple of image features extracted by the YOLOv5s model is large, thus making it difficult for the deep feature map to learn small target feature information, which causes missed and inaccurate detection of small obstacles in conventional YOLOv5s. Accordingly, a small target prediction layer is introduced to the conventional YOLOv5s model, and the shallow feature map of 152 × 152 scale in Backbone and the deep feature map of 152 × 152 scale in Neck after feature fusion are concatenated to increase the detection effect of the model for small obstacles.GIoU employed in the conventional YOLOv5s model can also measure the prediction results when no overlapping area exists between the Pre and the GT. However, the LGIoU has certain problems. In accordance with Eqs. , whe, wheGIoUGIoU to CIoU loss (LCIoU), and a power index α is added to the LCIoU to obtain α-CIOU loss (Lα-CIoU)28. The LCIoU considers the overlapping area, center point distance, aspect ratio, and penalty term of the Pre and the GT at the same time, making the bounding box regression more stable and achieving faster convergence. The equation of the LCIoU is expressed as Eq.  represents the center distance between the Pre and the GT; c expresses the diagonal length of the minimum closure area of the Pre and the GT; β represents a weight function; and v is adopted to measure the aspect ratio. The equation of β and v is expressed as Eqs. , whe, wheGIoUα (α = 3 in this study), the detection layer of the model exhibits higher flexibility in bounding box regression, thus effectively increasing the accuracy of bounding box regression without additional training or reasoning time for the model. The calculation equation of the Lα-CIoU is expressed as Eq.  to remove redundant prediction boxes and retain the box with the highest confidence in the prediction stage. Compared with the conventional NMS method, the weighted NMS method can effectively improve prediction accuracy by weighting and summing up the information in all prediction boxes. However, the weighted NMS method has high computational complexity and low computational efficiency. Moreover, in the practical application of the harsh coal mine roadway environment, when the obstacles are dense or partially blocked, the effect of using the weighted NMS method is not ideal. Therefore, this study adopts the Cluster-NMS method as the non-maximum suppression processing method of the ODEL-YOLOv5s model. The Cluster-NMS algorithm exhibits a fast reasoning speed and better suppression effect of redundant prediction boxes, and it will not excessively increase the number of iteration rounds with the increase in the number of detected targets, thus effectively reducing the time complexity1 module of the Backbone, resulting in the addition of a new 152 × 152 prediction scale at the Head. The number of layers in the optimized YOLOv5s model is increased from the original 224 layers to 281 layers, and the weight size and reasoning time of the model are increased. The pseudocode for improving the model is shown in Table The structure of the ODEL-YOLOv5s model proposed in this study is illustrated in Fig. In this study, the obstacle detection image dataset required by the experiment is self-made, and the obstacles include miners, electric locomotives, and rocks. The special explosion-proof camera for coal mines is used to shoot on the transportation roadway about 750 m deep under Yuandian No. 1 coal mine in Huaibei City, Anhui Province. In order to increase the diversity of the dataset, the different positions, distances, angles, lighting conditions, and occlusion of obstacles were considered during shooting. A total of 2000 dataset images are obtained after post-processing, and some of the images of the dataset are shown in Fig. The LabelImg labeling tool is used to label the obstacles in the dataset images; that is, all the obstacles contained in the images were manually selected by the minimum enclosing rectangle and labeled with “Miner”, “E-L”, and “Rock”, respectively. Lastly, the labeled dataset is divided into training set and test set in a ratio of 3:1.Pi is the corresponding accuracy rate of n positive samples of a certain class of obstacles31.In this study, Precision, Recall, AP (average precision) and mAP (mean average precision), average detection speed, weight size and amount of calculation are employed as the indicators to evaluate model detection performance. To be specific, the Precision is adopted to measure whether the model is accurate in obstacle detection, and the Recall reflects whether the model is comprehensive in obstacle detection. The AP represents the average detection accuracy of the model for a class of obstacles, and the mAP refers to the average detection accuracy of the model for all kinds of obstacles. The calculation formulas are shown in –10), wh, wh10), The CPU of the computer employed in this experiment is an AMD Ryzen 7 5800X, and the main frequency is 3.8 GHz. The GPU is an NVIDIA GeForce RTX 3060 with 12 GB of video memory. The computer uses the Ubuntu 18.04 operating system, equipped with Cuda 11.0 and Cudnn 8.04 for GPU acceleration. Furthermore, the detection model is built on Python 1.7 and Python 3.6.Before the training of the model, the training parameters of the configuration file are adjusted to yield the optimal model. The input image size is set to 608 × 608, the iteration batch size is 16, the momentum is 0.937, the decay is 0.0005, the learning rate is 0.1, and the number of iterations is set to 300.The training curve changes of the ODEL-YOLOv5s model and the conventional YOLOv5s model are presented in Fig. In this study, a variety of improvement measures are adopted to enhance the detection performance of the YOLOv5s model for obstacles in the harsh coal mine roadway environment. In order to further verify the effectiveness of each improvement measure, the effect of each improvement measure is analyzed on the detection performance of the model through ablation experiments, and the experimental results are shown in Table Model A is the YOLOv5s model without any improvement measures, and its mAP is 95.2%; the AP of small obstacle rock accounts for 89.2%; and the average detection speed reaches 76.4 FPS.Model B performs data augmentation processing based on model A. Compared with model A, the mAP of model B is increased by 1.0%, the AP of small obstacle rock is increased by 2.4%, and the average detection speed is increased by 0.8 FPS.Model C adds the CBAM attention mechanism based on model B. Compared with model B, the mAP of model C is increased by 0.7%, and the AP of small obstacle rock is increased by 1.8%. The addition of CBAM slightly increases the amount of calculation in the model, resulting in its average detection speed falling to 71.5 FPS.Model D adds a small target detection layer based on model C, so the model is improved from the original three-scale prediction (3-pre) to four-scale prediction (4-pre). Compared with model C, the mAP of model D is increased by 1.0%, and the AP of small obstacle rock is increased by 2.7%. Due to the increase in the number of layers and reasoning time of the model, the average detection speed of the model is reduced to 58.6 FPS.Model E improves the loss function based on model D. Compared with model D, the mAP of model E is increased by 0.4%, the AP of small obstacle rock is increased by 0.1%, and the average detection speed does not change.Model F uses Cluster-NMS based on model E. Compared with model E, the mAP of model F is increased by 0.6%, the AP of small obstacle rock is increased by 1.7%, and the average detection speed is increased by 1.6 FPS. Model F integrates all the improvement measures in this study, and each improvement measure improves the detection accuracy of the model to varying degrees.To visualize the difference in detection results between ODEL-YOLOv5s and other mainstream target detection algorithms, these algorithms are trained and tested based on the same self-made dataset, and the detection results are listed in Table In order to verify the detection effect of the ODEL-YOLOv5s model in real coal mine roadway scenarios, the detection results of the conventional YOLOv5s model and the ODEL-YOLOv5s model are compared and analyzed. The comparison of detection results between the two models is illustrated in Fig. For the detection effect of the conventional YOLOv5s model, the obstacles of the miner and rock are missed in an environment with insufficient light, as shown in images A1-B1. As depicted in images C1-D1, the partially blocked miner is missed in image C1, and the partially blocked electric locomotive is mistakenly detected as a miner in image D1. As depicted in images E1-F1, missed detection occurs in the images of multiple small obstacle rocks. According to the images G1-H1, a redundant prediction box appears in the detection of the miner in the case of relatively dense obstacles.For the detection effect of the ODEL-YOLOv5s model, as shown in images A2-H2. Due to the specific improvements made to the model according to the environmental characteristics of the coal mine roadway in this study, the detection ability of the model for obstacles is enhanced. Therefore, the obstacles can be detected accurately in the different environmental scenarios in which they are located. The comparative detection results in Fig. To further validate the detection ability of the ODEL-YOLOv5s model, the images are collected in the Guqiao coal mine in Huainan City, Anhui Province, and a test set of 1000 images is established. The conventional YOLOv5s and ODEL-YOLOv5s models are tested on the 1000 images, respectively, and the total number of obstacles detected by the two models is counted. The detection results are shown in Table The obstacle identification and detection of existing driverless electric locomotives in the harsh environment of coal mine roadways is subject to the problems of low detection accuracy and slow speed. In accordance with the characteristics of obstacles in coal mine roadways, the ODEL-YOLOv5s model is proposed for obstacle detection in coal mine driverless electric locomotives. The main conclusions are as follows: (1) Increasing the diversity of obstacle features by image augmentation processing on the self-made dataset can effectively increase the detection accuracy of the model. (2) Introducing the CBAM attention mechanism in the model to redistribute the weights of target and non-target regions in the image makes the model more focused on the obstacle information, thus further increasing the detection accuracy. (3) Adding a small target detection layer in the model to further integrate the shallow features with the deep features, which effectively facilitates the detection of small obstacles in the model. (4) By optimizing the loss function and the non-maximum suppression method, the regression accuracy of the prediction boxes is improved, and the redundancy of the prediction boxes is effectively reduced. (5) The mAP of the proposed ODEL-YOLOv5s model is increased from 95.2 to 98.9% compared to the conventional YOLOv5s; the AP of small obstacle rock is increased from 89.2 to 97.9%; the detection speed of the model is 60.2 FPS; and the weight size and computation amount are 15.9 Mb and 27.6 GFLOPs, respectively. Compared with the existing mainstream target detection algorithms, the ODEL-YOLOv5s model shows the advantages of high detection accuracy, fast detection speed, and small model memory, which can effectively ensure the safe operation of coal mine driverless electric locomotives.


The accurate estimation of a 3D human pose is of great importance in many fields, such as human–computer interaction, motion recognition and automatic driving. In view of the difficulty of obtaining 3D ground truth labels for a dataset of 3D pose estimation techniques, we take 2D images as the research object in this paper, and propose a self-supervised 3D pose estimation model called Pose ResNet. ResNet50 is used as the basic network for extract features. First, a convolutional block attention module (CBAM) was introduced to refine selection of significant pixels. Then, a waterfall atrous spatial pooling (WASP) module is used to capture multi-scale contextual information from the extracted features to increase the receptive field. Finally, the features are input into a deconvolution network to acquire the volume heat map, which is later processed by a soft argmax function to obtain the coordinates of the joints. In addition to the two learning strategies of transfer learning and synthetic occlusion, a self-supervised training method is also used in this model, in which the 3D labels are constructed by the epipolar geometry transformation to supervise the training of the network. Without the need for 3D ground truths for the dataset, accurate estimation of the 3D human pose can be realized from a single 2D image. The results show that the mean per joint position error (MPJPE) is 74.6 mm without the need for 3D ground truth labels. Compared with other approaches, the proposed method achieves better results. It has always been the goal of human society to allow machines to perceive and obtain information from the outside world through vision like human do. With the rapid development of human–computer interaction and computer vision technology, the issue of how to make machines understand and analyze human actions and behaviors correctly has become important. In this context, the task of human pose estimation was developed . The 3D In practical applications based on 3D human pose estimation, human motion information and joint positions can be obtained by wearing sensors and other devices, and the joint positions can be calculated by collecting depth maps through Microsoft Kinect . HoweverAt present, the mainstream 3D human pose estimation algorithms can be divided into traditional and deep learning methods. In the early days, traditional human pose estimation methods generally included a detector of human body parts that was based on a graph structure and a deformable part model, and described the spatial relationships between the body parts through a graph model. The extracted features were mainly set manually, such as HOG, SIFT and other features ; these aWith the advent of the era of big data, deep learning approaches have been rapidly developed for the field of computer vision, and research on human pose estimation has shifted from traditional methods to deep learning methods. Deep-learning-based pose estimation methods mainly use convolutional neural networks (CNNs) to extract human pose features from images, thus avoiding the need for the manual design of image features. In 2014, Li et al.  first shAlthough existing methods have achieved remarkable results, there are still significant challenges in regard to 3D human pose estimation at the application level. Since the most commonly used 3D datasets were captured by motion capture systems (MOCAPs) in controlled laboratory environments, the cost of 3D ground truth labels is high, and the background of the image is single. Network models trained on these datasets cannot easily be extended to outdoor and complex environments with significant occlusion of body parts. However, data without 3D labels are easily available, so the question arises as to whether we can train a network model using data without 3D ground truth labels. To solve this problem, we construct a network model for 3D human pose estimation in this paper, and use a self-supervised training method to estimate 3D human pose accurately from images without 3D labels.We present a 3D human pose estimation network model called Pose ResNet, which integrates the WASP and CBAM modules into the base network of ResNet50. We use epipolar geometry to construct 3D labels to realize self-supervision, hence solving the problem of obtaining 3D ground truth labels for datasets for 3D pose estimation technology. Our model is pre-trained on a 2D dataset and then undergoes further self-supervised training on a 3D dataset without 3D labels after migrating the trained model parameters, and synthetic occlusion is used to enhance the training images. Without the need for 3D ground truth labels, the accurate estimation of 3D human pose is realized from 2D images. (1)A network model called Pose ResNet is designed for 3D human pose estimation. This model is based on ResNet50 and WASP, and a CBAM attention mechanism is introduced to increase the receptive field and select the important pixels in a fine-grained way.(2)This model uses the strategies of transfer learning and data enhancement for training, which not only improve the accuracy of joint detection but also enhance the generalization performance of the model. Transfer learning involves a transfer from 2D to 3D data, while data enhancement refers to the synthetic occlusion of images in the 3D dataset.(3)A set of 3D labels constructed through an epipolar geometric transformation between multiple views are used to train this model. Without the need for 3D ground truth labels, this model realizes true self-supervised training.The contributions of this study are as follows:The rest of this paper is organized as follows: In In this paper, a single-stage method is used to estimate 3D pose directly from an image, and self-supervised method is used in training. This section will give an overview of the deep learning method of human pose estimation from two different perspectives: the number of model stages and the supervision mode.In recent years, CNNs have made significant achievements in the field of image processing, and deep learning technology has been used to meet the need for pose estimation in practical applications more effectively. Three-dimensional human pose estimation methods based on deep learning are mainly divided into two categories based on the number of stages: single-stage methods, which train the neural network to regress joint locations directly, and two-stage methods, which decompose the 3D pose estimation task into two independent stages, involving the estimation of 2D poses and then lifts them into 3D space.Many papers have adopted single-stage methods, such as ,11,12,13Compared to single-stage methods, which directly regress 3D coordinates from images, two-stage methods can also achieve good results. Zhou et al.  proposedThe successful application of deep learning in computer vision relies heavily on large amounts of fully labeled data, although labeled data with high confidence are sometimes difficult to obtain. Due to the high cost of acquiring 3D annotations for 3D pose estimation datasets, weakly supervised and self-supervised methods have become research hotspots in recent years.Many researchers ,24,25,26Existing 3D human pose estimation methods usually require 3D labels or camera internal and external parameters and rely heavily on consistency or adversarial loss to generate supervised signals, which are weak and can affect the performance of the model. Unlike the above existing methods, the supervised signal of our method is a strongly supervised signal, which is directly supervised with pseudo-labels calculated by an epipolar geometric formula, without using 3D labels or external parameters of the camera, and the trained network model is capable of 3D human pose estimation from monocular images.As a 3D human pose estimation model based on self-supervision, Pose ResNet is a dual-branch structure consisting of upper and lower branches. As shown in As shown in The key joint detection module of the 2D Pose CNN obtains the 2D pose The self-supervised learning of this model involves mining and constructing the supervision information from the data without manual annotations, and training the network based on the supervision information. Self-supervision in this case is achieved by the epipolar geometry module of the upper branch. As shown in The attention mechanism module operates similarly to the selective visual attention mechanism of humans. The aim is to select information that is more important to the current target task from all of the information that is available, thus effectively suppressing useless information and greatly improving the efficiency and accuracy of visual information processing. In the estimation of a human pose, due to the presence of complex image backgrounds, the spatial location information of the human pose features in the image plays an important role in accurately identifying the joints. Attention mechanisms can be used to suppress redundant background information and select the important pixels in a fine-grained way, thus making the network pay more attention to the joint information of human body. In this study, a CBAM  that comIn this paper, the CBAM attention mechanism, an attention mechanism module that combines spatial and channel information, is introduced before Layer 1 and after Layer 4 of the basic ResNet50 network . The strAs shown in The CA submodule weights the channel dimensions of the input feature map, its structure is shown in The SA submodule weights the spatial positions of the input feature map, its structure is shown in In order to capture the multi-scale context information of the image, expand the receptive field and make the detection of joint position more accurate, the WASP module  is addedHence, occlusion is a difficult problem when estimating the 3D pose of the human body. Expanding the receptive field enables the network to learn the relationships between the joints of the body using context information, which is effective for the inference of occluded joints. However, the differentiation between local information of the joints in the background is weak, which can easily cause local confusion. Expanding the receptive field makes the range of the visible image larger and enhances the differentiation of the local information on the joints.Before applying the WASP module, we first introduce atrous convolution, which is a way of increasing the receptive field. Atrous convolution means injecting atrous into the standard convolution to increase the receptive field. As shown in The WASP module samples the input in parallel with empty convolutions of different sampling rates and combines the results together. The number of channels is then reduced to the expected value through 1 × 1 convolution, as shown in At present, the lack of large outdoor datasets and datasets with special types of poses  constitutes the main bottleneck to the development of 3D pose estimation models. This phenomenon mainly arises because 3D datasets are built with MOCAP systems, which are suitable for indoor environments but require complex devices with multiple sensors and tights that are impractical for outdoor use. In this paper, we propose a solution to this problem by using two large public datasets, namely an outdoor dataset called MPII  and an iMPII , a datasThe Human3.6M dataset  is the lThe MPII dataset was used for pre-training, and the Human3.6M dataset was then used for self-supervised training. In the MPII dataset, the skeleton topology has 16 joints connected according to certain relationships, while in the Human3.6M dataset, the skeleton topology has 17 joints. The skeleton topology finally output by the network model has 16 joints, and joint 7 (spine) in At the training stage, synthetic occlusion  was usedocc) to synthesize training images with random occlusion [occ) was set to 0.5, and the degree of occlusion was between 0% and 70%. Examples of synthesized occluded images are shown in Synthetic occlusion was applied using the Pacal VOC dataset. Segmented objects extracted from the Pacal VOC dataset were filtered to remove people and objects marked as difficult or truncated, and the remaining 2638 objects were pasted into random positions in the Human3.6M dataset with a certain probability R and T are the extrinsic parameters of rotation and translation of the camera.Let the 2D coordinate of the (b)R for this camera is constant. The corresponding joints in F can be obtained.Without using the external parameters of the camera, we can assume that the first camera is located at the center of the coordinate system, meaning that (c)K and F into Equation (5), we obtain the essential matrix E. By using SVD to decompose E, we get four possible solutions to R.By substituting (d)By substituting the 2D coordinates into the epipolar geometry triangulation in Equation (6), we obtain the 3D coordinates of the corresponding joints by polynomial triangulation, and cache them as 3D labels, Epipolar geometry describes the internal photographic relationship between the two views, which is independent of the external scene environment, and is only related to the internal parameters of the camera and the relative pose between the two views. The upper branch of the model uses epipolar geometry to obtain the 3D labels i represents the number of joints, d is the scale factor of the human body and T is the threshold value. The normalized distance is the Euclidean distance between the predicted value of the joint and its corresponding ground truth normalized by the human scale factor. The normalization of the MPII dataset uses the current human head diameter as the scale factor, and the PCK threshold set to 0.5 in the paper means that the Euclidean distance between the detected point and its corresponding ground truth ≤ 0.5*head diameter is considered when the estimation of the joint is correct. For different datasets, the normalized scale factors may be different.The percentage of correct keypoints (PCK)  is the ms in the s in the s and “||.||2” means the L2 norm, that is, the Euclidean distance. Before evaluation, the estimated and real coordinates of the root joint were aligned via rigid body transformations such as translation and rotation.MPJPE  is the mThe experimental environment was a Linux system, and the experiment was carried out on an NVIDIA 2070 (8G) graphics card. The network models were built in Pytorch, and the ADAM optimizer was used to optimize the model. The initial learning rate was set to 0.001, the batch size was 16 and the number of iterations (epochs) was 130.In this paper, we present a self-supervised 3D human pose estimation model. An example of the estimation results on the Human3.6M dataset is shown in To validate the accuracy of the 3D human pose estimation model based on self-supervision in this paper, comparative experiments were carried out from the following four aspects.In order to demonstrate the advantages of the proposed method, it was compared with other existing fully supervised methods. A fully supervised method is one that uses the ground truth labels of the dataset during training. Due to the high cost of 3D annotation, it is very difficult to obtain strong supervised information in many cases, and weakly supervised and self-supervised methods ,40,41,42In order to verify the influence of different network depths, we compared the experimental results from residual networks with different depths at the pre-training stage. The higher the accuracy of 2D pose estimation at the pre-training stage, the better the results after migration and the closer the pseudo-label calculated by the epipolar geometry to the ground truth label. Hence, the accuracy of pre-training has a significant influence on the final experimental results. As shown in In order to verify the effects of transfer learning, the use of occlusion to enhance the training data, and the introduction of the CBAM and WASP modules to the network, a set of ablation experiments was designed. The results are shown in It can be seen from Given that obtaining 3D data for human pose estimation is a challenging problem, this paper proposes a method to solve this problem by using epipolar geometry to construct 3D labels for self-supervised learning, achieving excellent results. The network uses ResNet50 as the basic network in the feature extraction module. In order to learn more fine-grained features, a CBAM module combining channel attention and spatial attention is also introduced into the ResNet50. For the extracted features, the WASP module is used to capture multi-scale context information from the image to increase the receptive field. The model also uses two learning strategies, transfer learning and synthetic occlusion. The final MPJPE on the Human3.6M dataset was reduced to 74.6 mm.In future research, the model will be further optimized for the small target problem in order to improve the applicability to natural scenes, in which the area represented by the person in the image is too small or the subject is not in the middle of the image.


To investigate the void mesostructure in porous asphalt mixtures (PA), computed tomography (CT) and Avizo were utilized to scan and reconstruct the three-dimensional (3D) void model of PA-16 specimens. The void mesostructure of the specimen was quantitatively characterized through the anisotropy evaluation index. The equivalent pore network model (PNM) was extracted using the medial axis method. Based on the PNM model, the topological structure of the specimen and the morphological characteristics of the connected pores were analyzed. The results showed that the void anisotropy evaluation method can reflect the microscopic morphology of voids in porous asphalt mixtures. The cross-sectional porosity of representative elementary volume (REV) is mainly distributed between 20% and 25%, and about 90% of the macropores have a diameter between 0.5 mm and 3 mm. The distribution of cross-sectional porosity is uneven along the REV height direction. As the smallest cross-section of the seepage path, the equivalent radius of the throat is mainly between 0.1 mm and 1.5 mm, which is much smaller than the equivalent radius of the pore. The topological structure of pores is quite different, and their coordination numbers are mainly concentrated within 18. The pores with coordination numbers 1 to 10 constitute the main body of the pores inside REV, accounting for over 98% of the total number of pores. In addition, the permeability calculation results show that there is a significant difference in the permeability of each axis of REV compared to the total permeability of the superpave gyratory compactor (SGC) specimen, which illustrates that the permeability distribution presents an obvious spatial anisotropy. This study effectively reveals the heterogeneity of the 3D void morphology of porous asphalt mixtures, and it provides a reference for a better understanding of the void flow rules in drainage pavements. Porous asphalt (PA) or open-graded friction course (OGFC) is an environmentally friendly road material due to its noise reduction properties, cleaner rain runoff water, and improved traffic conditions in rainy weather. The performance of a porous asphalt mixture is dependent on its microscopic void (pore) features, which may refer to the content and distribution of its constituents . As we aDetermining the composition and mesostructure of non-transparent objects through advanced image technology has become an effective method for characterizing the microscopic pore structure of porous media . Since tWith the rapid development of digital image processing technology, the intersection of multi-disciplinary information in different fields is becoming increasingly close. It is no longer enough to rely on a single field or technology to solve complex problems such as numerical modeling of heterogeneous composite materials and coupled seepage of porous media . Avizo iThe Pore Network Model (PNM) is an equivalent of the complex pore space structure inside porous media, and it is an abstract model of the pore morphology of real porous media . The modIn view of the fact that the void structure of a porous asphalt mixture has an important influence on the transmission of fluid within it, the study of the void morphology of the mixture will help to essentially reveal the mecroscopic mechanical behavior and medium transmission characteristics of PA . We will(a)As shown in Porous media are solid materials that contain a large number of voids, which are widely present in nature, human production, and life, such as soil, rocks, animal and plant organisms, PA pavements, etc. Xiong et al.  and Luo (b)The space in a porous medium is filled with a type of substance or multi-phase substance, where at least one phase of the substance is non-solid and can be a gas or liquid phase. The sum of interconnected solid-phase units in porous media is called the solid-phase matrix (skeleton or porous matrix), and the remaining components are called voids or pores.(c)As a porous medium, the pore structure of a porous asphalt mixture can be divided into three types according to the type of connection between the pores and the external space, namely connected pores, semi-connected pores, and isolated pores . For porGenerally, the differences in mechanical properties of materials in different directions are called anisotropy. According to the different causes of anisotropy, anisotropy is divided into two forms: intrinsic anisotropy and stress-induced anisotropy ,28, whicAlthough the above two forms of anisotropy exist in asphalt mixtures, when analyzing the void mesostructures of drainage asphalt pavement, if the structural destruction process of the mixture is not involved, only the intrinsic anisotropy of the porous asphalt mixture needs to be considered . In addivA is the void area in a single-slice CT image (mm2), A is the total area of a single-slice CT image (mm2), ε is the porosity (%), and n is the total number of images.The porosity refers to the ratio of the void volume to the total volume of the sample. By counting the area of voids in a single CT image and the total image area, the porosity of each section is obtained by dividing the void area by the total area of the image. The total porosity of the specimen is the average of the porosity of all images. The specific calculation formula is as follows:z-axis) connected pores, it is required to track all connected pores from the top surface to the bottom of the specimen and eliminate all disconnected pores. As shown in i, j, m, and n are all image numbers.Pore connectivity is an important material property of PA mixtures, which is the channel and prerequisite for generating seepage . Due to In general, the grayscale value of the asphalt mixture in the CT image ranges from 0 to 255, and the values of each pixel represent the material density of the asphalt mixture at that location. If the pixel points are pores, the corresponding value in the binary value is 0. This principle is used to identify connected pores in each section of the image in the 3D model. When the value is 0, the nearest 8 values around it are checked. If there is a value of 0 among these 8 values, classify all values in the area with a value of 0 as one category and confirm that they are the same. Then, the connectivity of the vertical pores is determined through this algorithm. Starting from the first image on the top, for any pore object, check the values at the same location and the 8 values adjacent to that location in the next image. As long as there is a value of 0 in these areas, it is considered that the pore is connected in these two images, and these two pores are marked as the same pore. Repeat the same steps until the last image is verified. In addition, to calculate the tortuosity of the throat inside the specimen, repeat this step, starting from the last image of the bottom surface to the first image of the top surface. If the pores are not completely connected in these images, they are defined as closed pores, and the closed pores are eliminated from the calculation analysis.dpore) of the pore throat. The formula is as follows:Spore is the total area of connected pores in each cross-sectional image (mm2), and N is the number of connected pores.The pore diameter can characterize the volume characteristics of the connected pores of the drainage asphalt pavement . The lareL is the actual length of the water flow (mm) and L is the linear vertical length of the water flowing through the outlet and inlet of the specimen (mm).Due to the complex internal pores of asphalt mixtures, water mostly flows non-linearly within them. Tortuosity is used to characterize the nonlinearity of its seepage path. The formula is as follows:φ × h) is 100 mm × 100 mm. The coarse aggregate employed in this paper is limestone, and the fine aggregate and filler are granite. The gradation of the PA-16 mixture is designed based on the design steps of an OGFC [The test method refers to China’s Standard Test Methods of Bitumen and Bituminous Mixtures for Highway Engineering (JTG E20-2011)  and the  an OGFC . The aggThe composition and mesostructure of non-transparent objects can be non-destructively detected using X-ray CT technology. The radiation absorption rate of substances with different densities is defined as the CT number, and the conversion relationship between the density of the detected object and the CT number is established and converted into the corresponding grayscale value. For multi-component composite materials, CT scanning can be used to obtain and evaluate the mesostructures of each component . The CT As shown in Since CT images are easily affected by equipment and the external environment during imaging or transmission and often produce image noise, it is necessary to use the Median Filter function and Sobel operator of Avizo software to perform image filtering and image edge detection on the original CT images before image segmentation. The purpose of this operation is to smooth and suppress image noise. The 2D CT cross-sectional image after image noise reduction is shown in T1 = 68, T2 = 66, and T3 = 54, respectively. Considering that the more accurate the threshold, the higher the accuracy of the image processing, the better the 3D reconstruction effect of the pores, in order to improve the accuracy of the threshold, as shown in T4 = (T2 + T3)/2, T5 = (T1 + T3)/2, T6 = (T2 + T4)/2, T7 = (T2 + T5)/2, and T8 = (T2 + T7)/2 for iterative optimization of the optimal threshold.The Gonzalez iteration method, maximization of interclass variance method , and average gray value method are commonly used to calculate the grayscale threshold of CT images . The gloT1~T8 thresholds based on the image processing function of Avizo software. Compared with the test effective porosity of 21.76%, the calculated effective porosity corresponding to threshold T7 has the highest accuracy with an error of 0.09%, indicating that the 3D model reconstructed using threshold T7 has the highest applicability. Therefore, the optimal threshold for image segmentation is 64.T1~T8 different threshold segmentation processing.After the optimal threshold selection is completed, import all TIFF-format images used for 3D model reconstruction of the specimen into Avizo and perform image segmentation through the Interactive Thresholding function of the software. Taking the 2D cross-sectional image after image noise reduction in The three-dimensional visualization numerical model of the PA-16 specimen based on the Volume Rendering function of Avizo is shown in h × l × w). As presented in The real three-dimensional void mesostructure inside the porous asphalt mixture is extremely complex. In order to improve the extraction accuracy of the pore morphology evaluation parameters in the model, this study is based on the mesomechanical analysis method  and usesp is as follows:AV is the pore area in the single-slice CT image (mm2), and AT is the total area of the single-slice CT image (mm2).As shown in It can be seen from In addition, in order to quantitatively characterize the distribution of pores inside the PA mixture, the REV-3 was used as the research object, and according to the ER of macropores, pores were divided into five levels: <0.5 mm (level 1), 0.5 mm~1 mm (level 2), 1 mm~2 mm (level 3), 2 mm~3 mm (level 4), and >3 mm (level 5). As shown in In x, y, and z directions are 4.953 × 10−15 m2, 5.601 × 10−15 m2, and 1.405 × 10−14 m2, respectively. Compared with the total vertical permeability (1.168 × 10−14 m2) of SGC specimens, the permeability of the PA mixture exhibits significant spatial anisotropy. By comparing the topological structure of PNM, it can be found that due to the anisotropy of pavement pores, the more connected pores, larger pore throats, and greater permeability, the better the drainage performance of the pavement. Therefore, when paving drainage asphalt pavement, in order to ensure that the pavement has good drainage performance, in addition to ensuring that its average porosity meets the technical requirements of the regulations [z direction) is larger, while the permeability in the xy plane (x direction and y direction) is smaller, from the perspective of the bucket effect, in practical engineering applications, the drainage performance of the pavement can be improved by increasing the slope of the drainage pavement . The KC equation is as follows:k is the calculated permeability of porous media (μm2), p is the porosity of porous media (%), c is the Kozeny–Carman constant, and S is the specific surface area of the solid-phase pores.The permeability of REV in the 3D spatial coordinate system is calculated based on the Kozeny–Carman equation (KC equation) of porous media permeability in the field of rock seepage. As shown in ulations , it is aDue to the complex morphology of the connected pores inside the PA mixture, water flows non-linearly in the throat, so it is difficult to analyze the void structure of all its seepage paths in detail. For this reason, the minimum section area, equivalent diameter and channel length of the throat, and tortuosity are usually used to characterize the 3D microscopic morphological characteristics of connected pores . After cQ = K·A·(Δh)/L , when the water head difference between the upper and lower bottom surfaces of the sample is constant, the seepage amount Q is directly proportional to the cross-sectional area A perpendicular to the direction of water flow and inversely proportional to the seepage length L. Therefore, the smaller the minimum section area of the narrow place of the seepage channel, the smaller the seepage amount per unit time Q, and the greater the tortuosity of the seepage channel, the longer the fluid seepage path L, resulting in a longer time for the fluid to flow through the upper and lower surfaces of the specimen under the same pore pressure and flow rate conditions. As listed in 2 to 25.260 mm2, and the tortuosity is 1.42~1.66. The maximum values of the minimum section area and tortuosity are 601.43 times and 1.17 times their minimum values, respectively, indicating that the minimum section area of the connected pores of the porous asphalt mixture is very different, while the tortuosity parameters are relatively concentrated and the difference is small. Therefore, for the same kind of asphalt mixture, the influencing factor that causes significant differences in its permeability performance may be the difference in minimum section area. In order to improve the water permeability of the drainage pavement, during the material design and construction process of the drainage asphalt pavement, attention should be paid to increasing the cross-sectional area of effective pores inside the pavement structure while ensuring that the average void ratio or compaction of the pavement meets the specification requirements. For example, by adjusting the mineral aggregate gradation curve, reducing the amount of fine aggregate, increasing the roundness of coarse aggregate, limiting the flat-elongated particle content of coarse aggregate, and optimizing the mixing and compaction process, the connected void ratio or drainage performance of the porous asphalt pavement can be improved.Minimum section area and tortuosity are significant factors for controlling water flow and drainage time, respectively. In general, the flow of liquid in the seepage channel with connected pores as carriers inside the porous asphalt mixture is similar to the flow of pipes with smaller diameters or the flow of capillary bundles. According to Darcy’s law formula Based on MATLAB R2021b and Avizo 9.5.0 software, threshold selection and image processing were performed on the original CT scan image, image filtering was implemented, the optimal threshold for image segmentation was determined, and a three-dimensional numerical model of the specimen was constructed. The 3D model of the pore (REV-3) was extracted using mesomechanical analysis methods, model simplification, and the Axis Connectivity algorithm.The distribution pattern of cross-sectional porosity, the ER of the pore and throat, was quantitatively characterized through the anisotropic mesoscopic evaluation index. The results showed that the cross-sectional porosity is mainly distributed between 20% and 25%, and about 90% of the macropores have a diameter between 0.5 mm and 3 mm. The distribution of porosity is uneven along the REV height direction. As the smallest cross-section of the seepage path, the ER of the throat is mainly between 0.1 mm and 1.5 mm, which is much smaller than the ER of the pore.The topological spatial structure of pores is quite different, and their coordination numbers are mainly concentrated within 18. The pores with coordination numbers 1 to 10 constitute the main body of the pores inside REV, accounting for over 98% of the total number of pores. In addition, the permeability calculation results show that there is a significant difference in the permeability of each axis of REV compared to the total permeability of the specimen, which illustrates that the permeability distribution of REV presents an obvious spatial anisotropy. In order to further explore the anisotropic seepage mechanism of drainage asphalt pavement at the mesoscopic scale, in the follow-up work, a numerical simulation study of pore seepage will be carried out based on the three-dimensional reconstruction model of a real porous asphalt mixture specimen.The three-dimensional morphological characteristics of connected pores were analyzed based on parameters such as minimum section area, throat equivalent diameter, throat length, and tortuosity. The results exhibit that the minimum section area and tortuosity of connected pores have a greater impact on the seepage characteristics of porous asphalt mixtures. Compared with the latter, the minimum section area has a more significant impact on the water seepage performance of the PA mixture.This study attempts to explore and characterize the three-dimensional mesoscopic void characteristics of porous asphalt mixtures. The 3D pore model of the PA mixture was reconstructed utilizing CT images and Avizo, the pore network model was extracted from REV, and the anisotropic distribution characteristics of connected pores were analyzed through corresponding evaluation indicators. The significant results obtained in this study can be summarized as follows:


The characterization of the carbonaceous material obtained was based on the determination of the N2 adsorption–desorption isotherms at 77 K, the specific surface area with the BET procedure, and its internal structure by means of SEM images. The BET surface area values obtained as a function of the CO2/steam agent used were 911/1182 m2/g, 248/388 m2/g, and 800/1166 m2/g for bamboo, orange, and paulownia, respectively. The range of variation of porosity in paulownia residue generated after steam activation was 485–1166 m2/g, varying depending on the degree of maturity of the biomass used. Research has shown that both the type of activation agent used to produce AC and the degree of plant maturation of the precursor residue affect the quality and characteristics of the final product.The production of agricultural waste is associated with environmental problems and risks to public health. The general interest demands, as an ecological alternative, the proper management of waste generated by industrial activity through its transformation into value-added products. Carbonization/activation (2 h/2 h) at 700 °C in a vertical furnace (20 K/min), to produce biochar and activated carbon (AC) from bamboo, orange, and paulownia residue, was carried out in a laboratory facility with physical activation by CO Activated carbon (AC) is the most widely used adsorbent in industry due to its high and varied internal porosity, the ease and speed with which it retains certain pollutants, and the possibility of being reused.AC has a reticular crystalline structure with structural units consisting of parallel layers of carbon atoms arranged in regular hexagons, forming a three-dimensional network with spaces between its layers, whose walls generate a high capacity to adsorb substances . It is wThe production of AC from non-renewable raw materials such as coal, lignite, peat, or wood is costly and time-consuming . As an eBamboo is a hardy and versatile plant that has been used for centuries around the world for a wide variety of purposes. Paulownia is a fast-growing tree native to Asia that has achieved great popularity. Both species provide light and resistant woods suitable for reforestation and use in construction and decorative projects ,7,8,9.Orange peel is a solid waste produced during the processing of citrus fruit for juice production, with approximately 50–60% of the processed fruit becoming waste . HistoriThis reality highlights the importance of seeking solutions for the treatment of agricultural residue and represents an opportunity to obtain value-added products through their transformation into biofuels, chemical products, and other sustainable materials. In this sense, its transformation into AC by means of a carbonization process in an inert atmosphere followed by physical or chemical activation provides an efficient application for a waste whose disposal entails a series of economic and environmental problems. The pyrolyzation or carbonization process in the absence of oxygen is carried out in the usual range of 400–900 °C to remove volatile components and considerable amounts of tar produced by the plant matter, resulting in a solid residue (biochar) with a relatively high carbon content and preliminary porosity . ChemicaThermal treatment of biomass is one of the main techniques for its valorization in the form of solid (biochar), liquid (bio-oil), and gaseous (syngas) products. Biochar can be obtained, among others, from agricultural, forestry, urban, and seafood waste , promotiMost AC applications require a second stage of activation with mild oxidizing agents, such as carbon dioxide ,21 and sThe AC produced from biomass has been used for the adsorption and control of air pollutants, for the treatment of industrial gas emissions . Due to This paper presents the possibility of reusing agricultural, forestry, and industrial waste ,35,36 as2 and steam), as well as the degree of maturation/aging of the plant biomass  on the quality of the final charcoal obtained, has been studied.This paper provides information on the design of a facility for the transformation of bamboo, orange peel, and paulownia plant residue into biochar and AC. The thermogravimetric characterization carried out on the precursor residue has made it possible to choose the appropriate heating temperature value within a certain range. In addition to the temperature, the activation/pyrolyzation time and the heating ramp have been established based on numerous previous research studies. The effect of the gas used (COTwo lignocellulosic residues collected from the remains of bamboo cane (B) and paulownia wood of 1, 3, 5, and 8 years of growth or maturation  were used in this work. The third residue used was obtained from the remains of orange peel (O) from citrus processing.Manufacturer: Cotevisa (Bamboo and Paulownia)–Grupo García Carrión (Orange)Cities: Valencia–HuelvaCountry: SpainThe generation of these plant residues is associated with environmental problems such as accumulation in landfills, greenhouse gas emissions during the decomposition process, and water and soil contamination, as well as risks to public health.2 emissions. For this purpose, they will be subjected to a pyrolyzation process in an inert atmosphere with nitrogen followed by physical activation with carbon dioxide and steam to compare the development of their internal porosity. The flowchart in The development of AC from these wastes aims to seek an environmentally responsible industrial application while providing added economic value and contributing to the reduction of COSoil and non-organic residue were removed from the agroforestry residue used in this work, first manually and then by sieving. The bamboo (stem), orange (epicarp), and paulownia (bark) were blown clean of dust, leaves, and branches.They were subjected to a dehydration process at 105 °C for 10 h and were ground to increase their specific surface area in a blade mill (Retsch SM200) to a particle size of less than 0.2 mm .2, CO, H2, CH4, C2H6, and C2H4 [Thermal decomposition of the three main components of lignocellulosic biomass occurs at increasing temperature ranges, hemicellulose (250–350 °C), cellulose (325–400 °C), and lignin (300–550 °C). The gases released during the thermal degradation of the lignocellulosic material are primarily COand C2H4 . Celluloand C2H4 . The orange epicarp is mainly composed of sugars , protein , ashes (6.92%), crude fiber (10.01%), moisture (9.91%), and calcium . The ashes are mostly salts of organic acids that are decomposed by calcination; the remaining nutrients, not individually quantified, are fats, starch, and oils. A thermal susceptibility analysis was carried out and it showed that no exothermic reaction occurred when the sample was attacked with peroxide, indicating that it lacks any particular affinity or reactivity towards oxygen. Thermal analysis revealed the onset of slow exothermic processes at 108 °C and above, with oxidation reactions accelerating at 182 °C and net reactions at 202 °C and above . When heThe heating of the orange peel also produces an orange–brown condensed liquid containing a solid phase, deposited at the bottom of the container, and a liquid phase (bio-oil). The condensed liquid is analyzed by taking 3 mL and a liquid–liquid extraction with chloroform. The sample extracted under constant temperature and humidity conditions is passed through a 0.20 µm PTFE filter and analyzed by a gas chromatograph and ion trap mass spectrometer detection. In the chromatogram , the comThe process of carbonization and physical activation of the plant residue is carried out in a laboratory facility with supply bottles, mass controllers and gas mixers, a vaporizer–cooler system, an iron–chromium alloy  tubular reactor, and a vertical furnace.2 and CO2 used in the tests are supplied by pure gas cylinders (01) and regulated by means of mass flow controllers (02). The steam (V) is obtained by passing ultrapure water contained in a tank (03) through a vaporizer (04). The NIn order to prevent the gases introduced into the circuit from condensing and wetting the sample, the inlet duct is heated with an electric resistance and heating tape (06) from the outlet of the gas mixer (05) to the furnace inlet. The tubular reactor (07) where the inert atmosphere is established has a purge connection (08) to eliminate residual air and graphite gaskets at its ends to ensure a vacuum (09). The control of the furnace temperature (10) and the heating ramp applied to the material sample (11) is carried out by the furnace controller (12). At the height of the sample on the outside of the tubular reactor, three thermocouples (13) are located, with one in contact with the kanthal surface and the other two slightly separated (section S-S’). The thermocouple T indicates the temperature of the outer surface of the tubular reactor, while the thermocouples H and h, which are slightly separated from the surface, with lower thermal inertia, are used to control the temperature rise of the furnace . The furnace programming allows the setting of the heating temperature at any time, with it being controlled by the thermocouple M located in the center of the sample and connected to a digital thermometer (14). The solid carbonaceous sample is placed in the central inner zone of the tubular reactor and held by a slotted piece (15), with a small amount of quartz fiber in its upper zone and porcelain balls up to the bottom of the tubular reactor (16), thus allowing the circulation of gases up to the exit through the lower zone. At the reactor outlet, there is a fan (17), a collection vessel (18), a condensation system (19), a gas outlet pipe (20), and a condensate collection vessel (21).2, as well as the central zone between the furnace plates where the sample is placed for pyrolyzation and physical activation.2 is passed for 30 min, eliminating the presence of oxygen in the reactor and in the sample.The process to elaborate AC starts with the pyrolyzation stage, dehydrating and devolatilizing the sample. During this stage, there is a risk of overheating due to the exothermic process or even complete combustion of the carbon in the original material. To avoid this, a previous flow of 0.5 L/min of NThe heating rate for thermal decomposition of the biomass determines the final product obtained. A high heating ramp allows higher bio-oil yields to be achieved , while a2 is injected to maintain the inert atmosphere of the sample consisting of 10 g of bamboo, orange, or paulownia residue throughout the entire test.The internal temperature of the furnace is raised with a heating ramp of 20 K/min, until the maximum programmed set point temperature (700 °C) is reached. A flow of N2 is replaced by CO2 or steam, with the latter obtained by passing ultrapure water contained in a tank through a vaporizer :2 and steam produces endothermic reactions of 159 kJ/mol and 117 kJ/mol, respectively, which have easily controlled kinetics. It cannot be performed in the presence of oxygen (air), unless very low partial pressures are used, since the carbon–oxygen reaction, being highly exothermic, would cause ignition of the material without improving the porosity results.Activation with CO2 (approximately 0.402 mol/h) and 4.8 g/h for steam (approximately 0.267 mol/h), with this difference due to the fact that steam is more reactive than CO2. Once the programmed period has elapsed, the activation gas flow is cut off and the N2 flow is injected again, and with the furnace turned off, the sample is allowed to cool down to room temperature. Finally, the resulting material is stored in a desiccator for the corresponding characterization analyses.In an endothermic carbonization process, as opposed to an exothermic one, it is easier to control the transformation rate of the sample by means of the heat applied and the residence time in the furnace in the presence of an inert atmosphere. The activation gas flow rates used were 150 mL/min for COYield is the percentage of the amount of AC produced in relation to the amount of feedstock used in the pyrolysis process and is calculated on a dry basis according to Equation (4).The burn-off or percentage of solid residue removed during the carbonization and activation process increases with temperature and residence time in the reactor. It is calculated with Equation (5) using analytical techniques such as elemental combustion (CHN), atomic absorption spectroscopy, or infrared spectroscopy (FTIR).At higher burn-off, the volume of micropores and mesopores increases, but if it exceeds 70%, the process generally begins to lose microporosity .The characterization of the AC obtained with respect to yield, pore size distribution, and porosity development indicates the efficiency of the process followed during pyrolyzation and activation.n = 3) correspond to yield/burn-offs (Equations (4) and (5)) of 34.6% (±/0.32)/65.4%, 22.6% (±/0.47)/77.4%, and 31.3% (±/0.38)/68.7%, respectively, for the carbonaceous product obtained from bamboo, orange, and paulownia precursor residue.The results of the test with three replicates  and the amount of adsorbed nitrogen.The textural characterization of the samples is carried out by determining the N2 isotherms of the AC obtained after the pyrolyzation and physical activation stages using CO2 (C) and steam (V), at 700 °C for 2 h each, using bamboo, orange, and paulownia residue as precursor material (maturity grade P3).The shape of the isotherms obtained for the three residues fall into types I and IV according to the IUPAC classification corresponding to materials with microporosity (<25 Å) and mesoporosity (25–500 Å). The presence of a more developed hysteresis cycle is associated with capillary condensation occurring in its mesopores, and it limits absorption in the high-pressure range. The initial part of the type IV isotherm is attributed to monolayer formation in multiple adsorption layers as it follows the same trajectory as the type II adsorption isotherm. Additional tests have been conducted to determine whether the degree of maturation of the plant biomass can affect the quality of the AC produced. 2 molecules adsorb and desorb easily. This indicates that the solid surface has not developed a significant porous structure. The porous structure of the AC obtained may vary depending on the degree of aging of the precursor. As the precursor ages, the cell walls become stiffer and denser. The presence of a horizontal plateau in the isotherm and a barely visible hysteresis cycle indicates that the solid surface is relatively homogeneous and that N3/g) were obtained with paulownia residue that had a maturity grade of 3 years (P3). There is a decrease in charcoal quality as the biomass nears the end of its life cycle (harvesting), with a lower development in 8-year-old paulownia (P8) of its pore structure, BET area, mesopore volume, and external area. In this case, the shape of the isotherms (type I) shows a horizontal plateau and a barely visible hysteresis cycle. The initial steepest part represents the filling of the micropores and the plateau followed by a small slope is indicative of multilayer adsorption on the external surface area. The micropore filling process occurs at low relative pressures, which indicates a narrow micropore size distribution with no contribution from larger pores.The best nitrogen adsorption results .The morphology of the developed AC depends significantly on the precursor material. Bamboo cane is composed of long, thin fibers bound by a matrix of lignin and hemicellulose. Thermal decomposition and physical activation of cellulose and hemicellulose leads to the development and connection of porosity in the form of tubes. The presence of phenolic compounds and organic acids in orange peel may have influenced the reduced development and connection of the pores obtained with the thermal decomposition of cellulose and hemicellulose. During the physical activation of paulownia, its cell walls dilate, creating small cavities in its surface.The experimental data and results obtained in this work may be useful for the production of AC from vegetable waste and for other researchers to establish a reliable methodology for treatment and valorization.2. The AC produced from the plant biomass of bamboo cane, orange peel, and paulownia bark, using steam as the physical activation agent, developed a higher specific surface area and adsorption capacity than that produced with CO2 reacts mainly with the active sites located in the center of the pores, creating micropores, and only attacks the pore walls when the activation time becomes longer [2 and can diffuse faster through the pore network and gain easier access to existing micropores [2 is more selective toward creating micropores. Therefore, and in agreement with other research, steam is considered to be more reactive than CO2 under analogous conditions [It is believed that COs longer . Steam scropores ,62. Steanditions ,63.Relative mass ratios of organic and inorganic components vary with biomass type, growth environment, and harvesting time. The quality of the AC produced is influenced by the age and degree of maturation of the plant biomass of the recovered waste. Chemical degradation and alteration processes due to aging modify the properties and composition of the organic material. Cellulose and hemicellulose molecules undergo degradation and depolymerization processes, which can reduce the carbohydrate content of the biomass and increase the proportion of lignin and other compounds more resistant to carbonization and activation.This research has shown that both the type of activation agent used to produce AC and the degree of plant maturation of the precursor waste affect the quality and characteristics of the final product, and it is important to consider these factors in the design and optimization of the valorization processes.The differences in surface properties obtained depending on the degree of maturation of the plant biomass point to the need to extend this study to a larger quantity of residue and their applications as low-cost adsorbents.Machine learning is an artificial intelligence technique that involves the creation of statistical models and algorithms that can learn and improve through the analysis of large amounts of data. They can be used for the improvement of the characteristics of the obtained biochar with respect to its environmental applications , and to Future research should focus on exergoenviroeconomic analysis to assess the energy consumption, energy efficiency, and environmental impact  of the pThe amount and type of solid waste produced and liquid effluents generated, as well as emissions of greenhouse gases and other pollutants, depend to a large extent on the precursor material and the production process used. In addition, the environmental impact associated with the extraction and transportation of raw materials should be evaluated in comparison with the use of agricultural and forestry waste, etc. Finally, the analysis of the energy used for the evaluation of systems and processes  can prov