Background: Manufacturing markings printed on products play an important role in the handling and use of pharmaceuticals and perishable foods. Currently, optical character recognition, neural networks, deep learning-based methods, and combinations of these methods are used to recognize these codes.Methods: This systematic review was performed to find papers that can answer the following research questions: How have machine vision methods that can recognize product texts evolved over the past eight years? What are the most common difficulties in recognizing product texts? Articles published between 2012 and 2020 were systematically searched from Science Direct/SCOPUS, and Google Scholar in November-December 2020. Ten studies were eligible, with inclusion criteria: details about the recognition method used, performance analysis result, imaging method, product and the text printed on it.Results: Product text recognition methods have evolved significantly over the last two years to tolerate the most common difficulties in the field. This is due to the ability of the deep learning neural network (DNN) architectures such as convolutional neural networks (CNN) to extract and learn salient character features straight from packaging surface images. Four of the most recent methods use two consecutive deep learning networks, one detecting the text area based on an image captured from the product package's surface and the other recognizing the characters within. Furthermore, this paper presents solutions to the most common product text recognition difficulties.Conclusions: There were a limited number of studies that met the eligibility criteria for this systematic review. The study's aim was to evaluate the development of machine vision methods for recognizing manufacturing marking texts printed on the surface of products. The study results demonstrated how methods have evolved over time, beginning with optical character recognition, and advancing to methods which can recognize texts despite the field's most common problems. The manufactured product moves through the supply chain from the factory to its end users. The product requires primary packaging for its handling, secondary packaging may be required for storage, and tertiary packaging for its transport. The packaging has multiple functions, to protect the product during handling, storage, and transport, as well as prevent contamination and spoilage of the product. On the other hand, it should increase the usefulness and convey information for the many uses of the product., Production information, such as batch code, serial number, or expiration date, is printed on specific fields of the product, or its package at the manufacturing stage. Each of these consists of numbers and letters with a defined structure and length.The reported value of food and pharmaceutical production sold in the European Union (EU) in 2020 was over €105 billion and over €30 billion respectively. All perishable food products intended for human consumption must be marked with the date which their consumption is no longer considered safe. Likewise, finished medicinal products must be identifiable by the labels required by national law, including the expiry date in uncoded form and the batch number provided by the manufacturer.One key point in EU food safety legislation is the ability to trace products throughout the food chain. Instead of a human operator, machine vision could be used for product codes text recognition without contact with higher accuracy and speed.However, even today, the traditionally necessary step to check the expiration date information printed on packages is done by a human operator who manually picks up the package and checks the date. This is an everyday, monotonous, and high precision task, placing the human in an error-prone working environment.Converting these product codes into a machine-coded format is more difficult than optical text recognition of paper documents. However, it enables storage and processing of package-specific production data as well as search and extraction of codes and dates electronically. Common problems in recognizing the texts are caused by the complexity and deformation in the facet where the codes are printed, its problematic illumination, rich color information, insufficient contrast of the text, different printing types, and inconsistency of text characters.,– Although the package is regular in shape after the product has been manufactured, character distortion and warping during the storage duration can cause challenges for traditional OCR. Changes in physical conditions, such as different sizes and shapes of product packages, different positions, and angle of placement of the package in the camera view, and work environments lighting conditions make text recognition more difficult. When capturing images of packets moving on a conveyor belt, due to the motion blur caused by speed, the images may be blurred, making the recognition more difficult. The recognition of product codes from natural images is still a challenging task, as images may contain text with arbitrary perspective deformations in a complex background due to its unknown 3D position and orientation.,Existing optical character recognition (OCR) techniques work effectively only with clear characters in high-quality images on an uncomplicated background, while requiring character consistency in terms of format and viewing angle.,, However, training in the deep neural network (DNN) model required familiarity with the subject and a large number of experiments to determine what is a viable way to solve the text recognition problem in question.Neural networks modeled from brain structure are today very widely used in text recognition applications that require complex and large numbers of feature classification capabilities. At the same time, the graphics cards required for model training of neural networks are constantly evolving in terms of computing power and storage capacity. Several researchers propose neural network-based methods for detecting and recognizing packaging texts in recent studies., improving safety in the use of perishable food and pharmaceutical products,, and in developing faster and more accurate methods for the retail supply chain.,, Electronic processing of product-specific serial numbers is also suitable for electronic inventory management systems, development of intelligent product code recognition systems to help the daily lives of visually impaired people, development of intelligent household refrigerator food management systems, and also for the needs of metal industry to automatically verify the serial numbers of metal products at different stages of production.In recent years, machine vision technology has been increasingly used in a wide range of product codes recognition: it is used in the design of sustainable food systems to reduce food waste,For individual photosensitive sensors used in digital imaging, the dominant arrangement is a 2D array form. The imaging system see collects Imperfections in the orientation of the target surface, in its flatness, in the contrast of the text, in the consistency of the characters, or in the regularity of the printed text on the packaging surface complicates the recognition task. In addition to OCR, the subject of product code recognition has been approached in a number of methods. To extract representative features of characters, binary large object detection (BLOB), Histograms of Oriented Gradients (HOG), and Gabor-filtering, have been utilized, whereas classifiers and neural networks have been used to categorize them. Recently, the topic has been addressed utilizing end-to-end execution with Deep learning, such as fully Convolutional Neural Network, Mask R-CNN neural network, and Connectionist Text Proposal Neural Network (CTPN), for text region detection and recognition.,,,With good illumination, the imaging system captures a clear image of the flat surface of the packages. The computer see and its •Providing detailed knowledge of current algorithms for text recognition methods for product codes.•Systematically analyzing and presenting an overview of machine vision- based product code recognition techniques for each stage, , with a brief description of the techniques used in each stage.•Summarizing the performance of algorithms developed by various researchers and used and tested for product code recognition.•Providing knowledge of the associated imaging environment used and tested for product code recognition, with details on the surface properties of product packaging and printed texts.This research aims to advance the knowledge base in developing smart product handling methods for future researchers by:The systematic review method used in this study can be considered as somewhere between the traditional and meta-analysis review approaches. This method has been chosen to reduce the risk of bias and increase reliability. The evaluation was conducted in accordance with the guidelines known as PRISMA statement [Reporting guidelines].•How have machine vision methods capable of recognizing product texts evolved over the last eight years?•What are the most common difficulties in recognizing product texts?This work examines the literature to answer the following research questions:•The studies which include predefined keywords in the article title, abstract or keywords.•Those who deal with product codes text recognition which are based on computer vision.•Those that provided details of the performance analysis with details of the imaging method, product, printed text, and the method of text recognition itself.•Those studies that were published between 2012 and 2020.•Those that can be accessible.Answers are sought by formulating a research question and collecting data from a scholarly database, primarily Scopus, and secondly searching for related publications in the Google Scholar database. The search strategy for Scopus used in this study was a string of characters composed specifically of the following terms: batch code, batch, expiration date, expiry date, serial number, manufacturing, OCR, machine vision, computer vision, detection, recognition, and combinations thereof. The search was limited to literature published during the last eight years (2012-2020). Publications related to manufacturing, retail chain, and serial production were retrieved from the Google Scholar. The following criteria were used to select the appropriate studies to review:•The studies which are not written in English.•The studies with unidentified reference.•The studies published before the year 2012.The following criteria were used to exclude the studies from the scope of review:In total, 138 articles were extracted. The following is a detailed analysis of the product text recognition methods used in the 10 studies, as well as the recognition results achieved with them.et al., OCR is the process of converting text in images into a machine-coded form. The first step in OCR is to find the optical characters in the input image. OCR works by collecting detected dots (pixels) from an image, which are then compared to a model taught to the system. This is used to identify a detected character, which can be a letter, number, or special character. If characters form a group, this group is compared with possible grammatical words, and the correctness of the recognition can be automatically concluded. An OCR- based expiration date recognition system for the visually impaired is described in Penget al. It recognizes date codes of consumer products using cell phone camera and guides the user with voice feedback. This method works in two steps: First, the product barcode is detected. Next, the date code location information for the corresponding available facet and surface area is retrieved from the database and the date code is recognized therefrom by OCR. The accuracy of the proposed method reached 100% in all tests, while it is only 10-20% for the baseline system, which detects date-like texts from the wrong range and often misses the expiration date completely due to missing text location information.According to the Althobaitiet al. Method use source images captured from the packages with a standard digital camera. The expiration code is localized based on the local energy calculation of the images, the determination of the maximum energy difference, and the analysis of connected components. Characters found are binarized, and further segmented characters are convolved with a bank of Gabor -filters to extract three Gabor features: Fourier magnitude, imaginary response, and Gabor energy- response. Characters are classified by a sparse representation-based classifier using the Gabor energy -response. The method was tested with different backgrounds, code directions, and contrasts. It consistently located codes from images but suffered limitations with complex backgrounds and when the characters composing the code derived from location and isolation modules are very distorted. Moreover, it is sensitive to parameter selection, especially Gabor filter parameter settings. Furthermore, comparing the execution time of the proposed algorithm of 4.6 s with the execution time of the edge-based algorithm of 2.1 s in the corresponding task shows the slowness of the method.Gabor energy response based expiration code detection and recognition method for food packaging has been proposed in Zaafouri for expiration dates recognition of products. In pre-processing input image is binarized, skewness of the code is corrected, and the thickness of the touching characters are reduced. Character strings are segmented using a vertical projection technique to extract character images. Individual character images are normalized and convolved with a bank of 2D S-Gabor filters for feature extraction. Feature indexes consisting of the difference between local energy feature maps on subsequent orientation channels, the norm of the difference between subsequent magnitude responses, and the difference between subsequent complex moments magnitude of order one, are used in four filter orientations composing the feature vector used as input for multilayer neural networks for number recognition. The number of output nodes in the neural network is 10 corresponding to the number of digits, the number of nodes in the hidden layer is 50, the learning speed is 0.1, the network learns using the Backpropagation-method, and the number of maximum iterations is set to 5,000 in the method. The achieved average recognition rate reaches 99.3%. The method consistently locates the expiration code of images, and its degraded digit detection rate is high.A method based on Stretched Gabor features was proposed in Zaafouri, Sayadi, and Fnaiech for the recognition of serial numbers printed on labels. Numbers are detected using BLOB-algorithm with filters by color parameters. The KNN classifier used for recognition was first trained with the corresponding numbers. The classifier recognizes detected blobs by comparing them to its trained internal models. The method achieved a detection rate of 88%, and the recognition accuracy of the classifier was 100%. With the inexpensive Linux-based system, the processing speed of the method was 10 frames per second.The binary large object (BLOB) algorithm with K-nearest neighborhood (KNN) classifier was proposed in Mishra and Jainet al. a multidirectional illumination and image fusion method for recognition of metal stamping characters on metal surfaces of industrial products was proposed. In the method, the difference in surfaces grayscale values in four source images taken from different lighting directions is used to fuse the images and to eliminate the effect of background brightness differences with enhancing the contrast between the text and the background. Fused images’ character strings are binarized and segmented using the horizontal projection function. Further connected component labeling algorithm is used for single characters separation. For single character histogram of oriented gradients (HOG) -feature extraction, the block of four cells is traversed through the input image in horizontal and vertical directions. Direction and amplitude of the gradient are calculated in each cells in the block. Images’ spatial histogram is obtained by dividing the gradient direction into nine bins in each cell and merging them into a 36-dimensional block feature. By traversing the image, a feature matrix composed of all block features is obtained. The feature vector describing the features of the whole image is obtained by concatenating each row and column of the feature matrix. Backpropagation -neural network is used as a classifier for character recognition. The method achieved a recognition accuracy of 99.6% with an execution time of 2.4 s with a cell size of four pixels with stride three.In Xianget al. for food packages expiration date detection and recognition. For date region detection Fully Convolutional Network structure decomposing into three parts is used: First branch, the feature extractor stem composing of interleaving convolution and pooling layers is used to extract four levels of feature maps from the input image. Features from different scale levels detects date code regions with different sizes. In the second phase, the feature merging branch, the feature outputs from a different layers of the Feature extractor stem are concatenated, and convolution layer is applied to produce the final feature map. Final feature map is fed into the third branch, the output layer, which contains multiple 1x1 convolution operations to project 32 channels of feature maps into score map, geometry map, and angle map. Score map gives likely-hood that a pixel belongs to the expiry date region, and multi-channel geometry map defines the boundary of the text box, which can be either a rotated box or quadrangle. Network is trained based on the defined loss function using the adaptive moment estimation (ADAM) -optimization tool until performance improves. Location of the final text region area is determined by the locations of the values that are greater than the score map threshold. Geometries associated with these locations on the geometry map are then combined with location-aware non-maximum suppression (NMS) to determine the final text region. Tesseract OCR is used to recognize texts from detected expiry date regions. First, using the maximally Stable Extremal regions (MSER) algorithm, the extracted date code region is binarized with characters being differentiated from the background. Connected component analysis is used to find blobs representing different characters while filtering out small noisy spots. Each candidate blob boundary with the corresponding shape features is extracted for character classification. In the nearest neighbor (NN) classifier blob features are compared to the prototypes representing different characters and classified as the character for which the relative distance is smallest. The proposed system is trained and tested using different types of food package images taken in a natural food store environment. The method achieved a text recognition rate of 98%. However, text recognition errors occurred with blurred characters.A method based on fully convolutional network (FCN) and Tesseract OCR was proposed in Gong A controlled imaging environment was used for bottle imaging. Transparent and curved plastic bottles are back illuminated, with text area oriented directly to the camera. Mask R-CNN algorithm is used to detect the bottle from the image, returning the bottle shaped bounding box, mask, label, and score of the recognition. The bottle-shaped image is scaled to a predefined size and processed using a morphological gradient operation to outline the objects it contains and further binarized. To find the text area in the image, the white pixels it contains are morphologically processed to form rectangular shaped blobs. By only using the extreme outer contour extraction function, contours are extracted and drawn on a new black image. The properties of the bounding box areas of each contour object are verified, and the original image is cropped using the bounding box coordinates resulting from the included contours. Cropped image with texts in a bounding box is zoomed to twice the size of the original and processed with a sharpening operation before binarization, and morphological processing with the aim of connecting the dots, keeping the characters separate while expanding the number of character pixels. Characters are segmented from the image using vertical and horizontal projections. In post-processing phase often missing dot matrix character parts are reconstructed using morphological dilation. For feature extraction, post-processing phase images, used as features for classification, are equally padded and resized forming a 32x32 pixel square that conforms to CNN’s constraints. Segmented digits are recognized using the LeNet-5 CNN - architecture. Network is trained with a set of ~22500 images in 10 epochs and with batch size of 1000, achieving 97,5% test accuracy.CNN-based deep learning method for water bottle dot matrix characters recognition is described in Muresan, Szabo, and Nedevschi.et al. A set of three images is captured at a time in three orientations from a box moving on a conveyor. After pre-processing with motion blur removal of and image sharpening, pre-processed images are subsequently used for the text localization. The connectionist text proposal neural network algorithm is used to detect the text in the image, recognizing the lines of text as a series of fine text proposals. The methods vertical anchoring mechanism predicts the location and text/non-text scores of each fixed-width proposal. The localized text sequences are cropped, and the resulting image is enhanced and adaptively thresholded. Discontinuities in the pixels of the characters are removed before connected components-based contour detection, after which contours with very small width and unexpectedly large height are removed. Each of the contour features are extracted, contours are compared with each other, and groups are formed based on the features belonging within empirically selected values. The objective is to find the character contours for batch codes of a finite length. All characters of the localized text are sent in batches to the capsule-based modified caps net-network, whose structure consists of two feature blocks and two layers of capsules for recognition. Input features extracted by successive convolutions of feature blocks are used to create feature vector, which is then fed to capsule blocks for character prediction. The feature vector enables the network to learn the spatial relationship between features. The achieved recognition accuracy is 85.6% with the real world dataset and 91.3% with the synthetic dataset.End-to-end deep learning methods for batch codes recognition printed on cardboard boxes was proposed in Singh the combination of two deep neural networks for dot matrix printing recognition of food drink cartons is proposed. Faster R-CNN, used for expiration date digit location and recognition, first obtains the position and size of characters in an image. The system then scans the expiration date area using a raster scanning method and crops out the area of recognized characters in the image. Character recognition Le-Net network is used for character recognition from the cropped image. The system then combines the results of both neural networks to get a final result based on the spacing of the digits. The limited size of the training data set limited the method's recognition accuracy to 97%, according to the researchers.In Ashino and Takeuchiet al. Source images of the methods are captured in the real food industry/retail environment, which includes different colors/textures, and low-quality images. A fully connected CNN as in Gonget al. is used in the text region detection method. Text recognition is performed using CRNN-composed of three parts including the feature extraction part, bidirectional long short-term memory recurrent neural network (LSTM RNN) part, and transcription layer part. In feature extraction, convolution and pooling layers thereof are used to partition the input image into image patches. Feature vectors corresponding to the number of patches are fed to the bidirectional recurrent neural network with the LSTM unit to predict the label distribution. Recurrent layers in Bi-directional LSTM-RNN capture the contextual dependencies between consecutive image patches. Bi-directional LSTM-RNN operates on arbitrary length text sequencies, recognizing texts of different lengths in different formats. The transcription layer of the CRNN converts the predictions of the second LSTM-RNN into a label sequence that maximizes the conditional probability given by the bidirectional LSTM-RNN predictions. Comparing the text recognition performance of the proposed method with Tesseract OCR used to recognize similarly detected texts, the CRNN network performs better in recognizing blurry characters, with OCR being able to misclassify them.The dual DNN method, FCN for text region detection, and convolutional recurrent neural network (CRNN) for text recognition of food packaging is proposed in Gong, Product codes printed on products can now be recognized on surfaces that traditionally normally require human vision using current state-of-the-art methods. The PRISMA-based flowchart of this systematic review is shown inThe results are based on a detailed analysis of 10 studies published between 2012 and 2020 which presented state-of-the-art product code recognition methods.(1)Changes in the shape, size, position, and placement angle of the packages in the camera view.(2)Changes in the shape of the packaging surface on which the codes are printed.(3)Changes in the illumination of the packaging surface.(4)Low contrast between the text printed on the surface of the product and the background or its variation.(5)Inconsistencies in the character shapes of the text.(6)The effect of motion blur caused by the movement of the package.In this section, we examine the development of product text recognition methods over the past eight years. Based on the analyzed articles, six relevant aspects can be identified that affect the recognition accuracy:For the results, the text recognition performance, imaging methods, products, texts printed on them, and printing methods of the articles published during the research period were compared with those of the first year.Deep learning methods with two consecutive networks are the most tolerant to the most common problems in the field. Despite variations in physical circumstances, changes in the curvature of the package's surface, and changes in illumination, the study's deep learning algorithms recognize the characters. In a controlled imaging environment, they also recognize low-contrast characters, characters with irregular character formats, and images captured from moving packages. Recognition accuracies of the methods are despite these imperfections over 91%.Conventional recognition methods have evolved to tolerate variations in surface shape and illumination, as well as low contrast between the text and background, during the period of the research. On the other hand, the rate of recognition of regular characters on a plain background has increased. They have a recognition accuracy of more than 99%.Deep learning has outperformed other recognition methods in the past two years, while no conventional methods have been proposed. The following numbers of deep learning character recognition algorithms have been proposed:,,,, Four of them,,, use two separate deep networks for text area detection and character recognition.Five of them utilize deep learning to detect text regions.A significant result for this field is the comparison of the performance of conventional character recognition with deep learning when recognizing characters that are inclined, affected by lighting, and printed with low printing quality from food packaging images: The CRNN method has a recognition rate of 95.4%, whereas the Tesseract OCR method has a rate of 31.1%.,,,, included multi-directional illumination of the text area to enhance low-contrast characters, recognition of regularly shaped characters by a computationally efficient BLOB algorithm with the KNN classifier, character recognition based on energy differences in different areas of the image, and the OCR method. The performance of conventional recognition methods has improved in terms of the speed of recognition of clear characters in clear backgrounds, as well as in the recognition of degraded characters.In this research, deep learning is used to recognize characters in a wide range of food packaging, beverage cans, transparent water bottles, and moving boxes. Conventional recognition methods in the study dot-matrix character segmentation, deep neural networks in impaired character recognition, and the effect of pre-processing methods in improving general-purpose OCR performance.With the set search criteria, ten studies containing performance analysis were found, of which seven proposed methods for expiration date code detection, two for serial numbers, and one for batch code recognition. In addition to them, four research papers from the years 2012-2020 were examined in order to obtain a sufficient knowledge base of the field, they deal with: OCR performance in a variety of environments,This section will explore of which are the most common difficulties in recognizing product text. The search strategy used made state-of-the-art product text recognition methods available. Studies of the latest methods include solutions to overcome the most common problems in the field. Analysis of the research material provided answers to the second research question:The main problems in product code recognition are:Scene complexity: Variations in physical conditions while capturing images of product packages: Changes in the shape, size, location, and angle of placement of the packages. A natural scene image from package may contain text with arbitrary perspective deformation in a complex background due to its unknown 3D position and orientation.(1) To solve this problem,(2) Due to the change in the shape of the package surface, the intensity of the light varies in different locations, which is reflected as different shades of gray on the same surface, leading to an incorrect recognition result. To solve this problem,(3) Low contrast of text printed on the surface of the package. Variations in product label formats. Caused by uneven illumination of a complex package background (not a flat surface). Background colored texts. Complex background with writing style. To solve this problem,(4) Fonts and print style. Inconsistencies in character shapes in manual ink stamping and in dot matrix printed texts. Variations in printing styles such as blurred code due to manual printing, such as ink stamping.Some printing methods may produce blurry texts and texts without common features from any other font family. During to the storage duration, texts may be distorted and warped.(5) Motion blur due to movement:Motion blur caused by acquiring an image of the product package as it moves on the conveyor. To solve this problem,The answers to the research question have been found through an in-depth literature review of recent research and analysis of the papers included in the research.This paper provided a detailed literature review of state-of-art product code recognition methods proposed and tested in recent relevant studies. The research questions focused on finding solutions for the development of methods for product text recognition in the last 8 years, and for the most common problems of product text recognition.Recognition techniques were divided into a previous conventional recognition method period of 6 years, and a deep learning methods period into the last two years. Increased application of deep neural networks for product text recognition since 2018 has made possible to recognize inconsistent characters, detect and recognize text of different sizes in images captured in real-world conditions and recognize text from moving packaging.Methods with two separate consecutive deep neural networks has made it possible to recognize distorted text and irregular characters on surfaces exposed to light, even in low-quality images. In these methods, deep-learning neural networks have been defined for use in text area detection and in character recognition algorithms. The methods use this structure to extract and learn the features of text regions and characters from a large set of training images, and then recognize the characters in subsequent images using the model they have learnt. Such methods greatly contribute to the recognition of packaging texts in real-life conditions. Conventional recognition methods in the study included multi-directional illumination of the text area to enhance low-contrast characters, recognition of regularly shaped characters by a computationally efficient BLOB algorithm with the KNN classifier, and character recognition based on differences in energy in different areas of the image. The performance of conventional recognition methods has improved in terms of the speed of recognition of clear characters in clear backgrounds, as well as in the recognition of degraded characters.This study demonstrated that product text recognition techniques have evolved to address the most common research problems presented in the results section of this study.In the first phase of the study, a comprehensive literature search on state-of-the-art methods was carried out, which was analyzed, and the individual method details were presented with a recognition performance analysis. The results section answers research questions and presents the methods tolerances to the most common difficulties in the field, identified based on analyzes performed on research articles. At the end of the results section, the most common difficulties in this area are presented in detail, together with the proposed solutions from the literature.This research topic is relatively new, and although studies have been published in this area, there are not many. It would be of particular interest to find further studies where experimental results were obtained with real products and environments. Successful product text recognition also requires consistent thinking when designing the product imaging phase, how to capture an image of each package such that the source image's image analysis can effectively recognize the characters contained within it. Since objects of interest (characters) are imaged with visible light from the surface of the product, the research area itself, the development of recognition algorithms would be facilitated by a well-known standard imaging environment. Similarly, it would be useful for research development to have a data set in which the codes are printed using different printing methods. Such as laser and dot matrix printing, stamping, character pressing, and character pressing with ink marking. In addition, a research topic that deserves attention is the contextual handling of recognized characters. In this field of research, publications often propose solutions for the classification of digits, letters, or combinations thereof. In the packaging handling industry, there is a need to convert a variety of well-defined character sets into electronic form, so research should focus on the contextual understanding of different length codes. With OCR, which has been used for decades, this is done by comparing the result with the grammatical words.https://doi.org/10.17605/OSF.IO/8Z54TOpen Scientific Framework: Summary of References for Recent advancements in machine vision methods for product code recognition. A systematic review.•Summary of References Reviewed for Recent advancements in machine vision methods for product code recognition. A systematic review.xlsx (Summary of references).This project contains the following underlying data:Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).Data are available under the terms of thehttps://doi.org/10.17605/OSF.IO/CN42QOpen Scientific Framework: PRISMA checklist for ‘Recent advancements in machine vision methods for product code recognition: A systematic review’Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).Data are available under the terms of the Upon a thorough review of the manuscript, it is gratifying to note that the paper aligns with the required criteria for acceptance. The authors have demonstrated a comprehensive understanding of the subject matter and have effectively presented their research methodologies and findings. The study's focus on text recognition techniques in the context of pharmaceuticals and perishable foods is well-aligned with the aims and scope of our journal. Including traditional and modern recognition methods and a detailed account of extraction, segmentation techniques, and results showcases a rigorous approach to the research. Furthermore, exploring deep learning techniques and their outperformance in recent years adds valuable insights. Given these strengths, the paper is acceptable for indexing with minor revisions to address some points raised earlier. The authors' work contributes significantly to our understanding of text recognition and its applications and will undoubtedly enrich the scholarly dialogue in this domain.While the authors have concentrated on text instances associated with pharmaceuticals and perishable foods, it is worth acknowledging that benchmark datasets within the scene text detection community encompass many images featuring related content. The authors might consider incorporating recent deep learning methodologies with readily available pre-trained models on platforms like GitHub to augment the research. By evaluating these models on text instance images related to pharmaceuticals and perishable foods, the paper could provide a broader perspective on the performance of the chosen techniques within a more diverse context.To provide a more comprehensive understanding of the applications under study, it is recommended that the authors include qualitative results for the challenges that exist in these types of text instances. This could involve showcasing examples of successful text recognition instances from their research. Additionally, for each application, including detailed captions that specify the encountered challenges would enrich the readers' comprehension. Such qualitative insights can offer a nuanced view of the complexities faced in real-world scenarios, enhancing the overall impact of the paper.Addressing the challenge of occlusion in product text instance spotting and its potential impact on the performance of trained models could enhance the paper's comprehensiveness. Therefore, the inclusion of a discussion regarding this aspect would be beneficial. To further enhance the strength of the paper, the authors are encouraged to consider the following suggestions:Are the rationale for, and objectives of, the Systematic Review clearly stated?YesIs the statistical analysis and its interpretation appropriate?YesAre sufficient details of the methods and analysis provided to allow replication by others?PartlyAre the conclusions drawn adequately supported by the results presented in the review?YesReviewer Expertise:Computer Vision, Deep learning, Text Detection, and Recognition in the Wild.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This study reviews the text recognition techniques in last eight years particularly from the products related with pharmaceuticals and perishable foods. There were 138 search results returned to the authors from various sources and ten articles were shortlisted for this research. Overall the methods used by different researchers have been analyzed and presented systematically in this work. Research works using both the traditional recognition techniques like KNN and modern recognition techniques like deep learning methods have been analyzed. A detailed account of extraction methods, segmentation methods, recognition and results have been presented for all the studies reviewed in this research. This research also highlighted that some of the deep learning based techniques use a combination of two networks: one for text area detection and other for character recognition. The research presents a detailed discussion on different problems in text recognition that have been solved in recent years. The solutions to the various problems have been presented from the literature in forms of tables. This research concludes that the deep learning has outperformed the conventional methods in recent years.Using a deep neural network models, able to automatically learn effective features for text detection and recognition under variety of scenes. The paper claims to deal the text recognition for perishable food and pharmaceutical however it only cited the research using food and food packages. Pharmaceuticals have not been cited in any of the 10 research works. Overall the research work is acceptable though there are two points needed to be clear:Are the rationale for, and objectives of, the Systematic Review clearly stated?YesIs the statistical analysis and its interpretation appropriate?I cannot comment. A qualified statistician is required.Are sufficient details of the methods and analysis provided to allow replication by others?YesAre the conclusions drawn adequately supported by the results presented in the review?YesReviewer Expertise:Pattern Recognition, Fingerprint Recognition, Online Signature Recognition, Machine Learning and Deep Learning.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


To provide theoretical support for the safety control of rectangular pipe jacking tunnels crossing an existing expressway, a method for predicting the surface settlement of a rectangular pipe jacking tunnel is proposed in this study. Therefore, based on the high approximation of the BP neural network to any function under the multiparameter input, the PSO-BP mixed prediction model of the ground subsidence of the ultrashallow buried large section rectangular pipe jacking tunnel is established by taking into account the adaptive mutation method, adopting the improved particle swarm optimization (IPSO) algorithm with adaptive inertia weight and mutation particles in the later stage to determine the optimal hyperparameters of the prediction model. Through the case study of an ultrashallow large cross-section rectangular pipe jacking tunnel, this algorithm is compared with the traditional algorithm and combined with field monitoring data for analysis and prediction. The prediction results show that compared with the traditional BP neural network prediction model, AWPSO-BP model and PWPSO-BP model, the improved PSO-BP mixed prediction model shows a more stable prediction effect when the change in surface subsidence is gentle and the concavity and convexity are large. The predicted subsidence value is close to the actual value, and the accuracy and robustness of the prediction are significantly improved. Rectangular pipe jacking construction technology is often used in urban underpass tunnel projects because of its safe and reliable construction and low disturbance to the surrounding environment. As an increasing number of pipe jacking tunnels need to penetrate existing buildings (structures) closely, ensuring the stability of pipe jacking construction and reducing the impact of tunnel construction on existing buildings (structures) are hot issues at present. During the jacking process, the ultrashallow buried large-section rectangular pipe jacking tunnel inevitably disturbs the surrounding strata, changes the distribution of the stress field and displacement field of the strata, and finally affects the overall consolidation and settlement of the tunnel location with increasing time. The cause of formation deformation caused by pipe jacking construction is mainly that the formation damage disturbance caused by excavation cannot be compensated for in time, it is difficult to return to the initial state of undamaged disturbance, and a new three-dimensional equilibrium state can only be achieved through the necessary stress adjustment or change process. Therefore, it is of great significance to accurately predict the ground subsidence caused by rectangular pipe jacking tunnels to reduce the impact of engineering construction on the surrounding environment and ensure the safety of the project.4 can describe the general morphology of surface settlement and roughly believes that the lateral distribution of surface settlement generated by tunnel construction is approximately the normal distribution curve, but due to the large difference in the values of different strata empirical parameters, the two key parameters surface settlement trough width 9 often only considers the three factors of the frontal additional thrust of the pipe jacking tunnel, the friction between the pipe section and the soil mass and the soil loss, and it is difficult to consider the soil loss distribution and grouting situation. The calculation process is cumbersome and complex, which is difficult to use in actual engineering. The numerical simulation method13 can comprehensively consider the nature of the strata, the interaction between the pipe jacking machine and the strata, tunnel parameters, the location of the excavation surface, the tunnel lining and other factors, which provides a powerful visualization method for researchers to understand the failure mechanism inside the rock and soil mass. However, numerical simulation is intensive, laborious and laborious, and it is difficult to select geotechnical parameters and boundary conditions during the modelling process, which makes the analysis data different and uncertain. The model test17 method is widely used to study the evolution mechanism of settlement during pipe jacking construction, but the factors considered are limited and costly, so it cannot be used to predict surface settlement in real time. Moreover, due to the difference between the model test conditions and the actual engineering boundary conditions , the correction coefficient may have errors in the actual situation, and the conclusion needs to be verified by actual engineering.The existing surface settlement prediction methods mainly include empirical methods, analytical methods, numerical simulation methods, model test methods and artificial intelligence algorithm predictions. The well-known Peck formula in the empirical method18. Among them, the machine learning algorithm can learn the characteristics of the data through a sufficient number of sample inputs and then perform regression fitting analysis on the data to effectively analyse new inputs with similar patterns and make predictions19. By establishing a machine learning probability and statistics model, the internal connections of the input data can be automatically analysed, and the high-dimensional fitting of cross-cell and cross-latitude multiparameters can be realized. Moreover, the machine learning settlement prediction model is trained with fast calculation and accurate results, which meets the requirements of actual engineering to obtain surface settlement in a timely and accurate manner. Since the 1980s, some scholars have tried to use methods related to machine learning to solve practical tunnel engineering problems28. In recent years, artificial neural networks, support vector machines and random forest algorithms have become the main machine learning algorithms used to predict surface subsidence caused by shield tunnels. Ramezanshirazi29 and Tang30 used machine learning algorithms to effectively predict surface subsidence caused by tunnel construction. Chen et al.31 found that regression neural networks and random forest algorithms can perform best among the six machine learning algorithms and accurately identify the evolution of settlement caused by tunnels. Elbaz et al.32 established an improved particle swarm optimization algorithm (PSO) combined with an adaptive neural fuzzy inference system (ANFIS) based on the fuzzy C-means (FCM) clustering method for the prediction of the shield performance of metro tunnels. Cao et al.33 proposed a fully ensembled empirical model with adaptive noise length and short memory to monitor ground subsidence during tunnel construction, which can divide one-dimensional data into multidimensional data. It can be seen from the above that in the past tunnel surface settlement prediction problem, most of them chose to use several single algorithms for separate prediction, and the results were compared to illustrate the superiority of a certain algorithm.Artificial intelligence algorithms have powerful information processing capabilities, such as nonlinearity, high parallelism and high fault tolerance learning and generalization capabilities34 built the PSO-BP neural network, wrote the PSO-BP code, collected the historical container throughput data of Shenzhen Port, set the relevant parameters, and used the PSO-BP model to predict the container throughput of Shenzhen Port. Wan et al.35 proposed a BP neural network adaptive PID controller based on particle swarm optimization (PSO). Using the Chinese national standard variable rate fertilizer seeder control system, the system's performance indicators, such as fertilizer rate adjustment range, fertilizer rate adjustment response time and fertilizer rate control accuracy, were evaluated, and the variable rate fertilizer performance parameters were successfully improved. Shu et al.36 proposed a PSO-BP neural network to evaluate the customer perceived value of new Chinese clothing. The experimental results show that the PSO-BP neural network can accurately evaluate the customer perceived value of the new Chinese style clothing, and enterprises can improve the product design quality through the evaluation index and model of the customer perceived value of the new Chinese style clothing, thereby improving the sustainable competitive advantage of enterprises and finally realizing the sustainable development of the new Chinese style clothing industry.To improve the prediction accuracy, this paper intends to use the combination of the two algorithms to build a predictive model to improve the prediction accuracy and robustness. In this paper, an improved particle swarm algorithm is proposed to balance the local and global optimization ability of particles and enhance the ability to jump out of a local minimum in the late stage of particle search by adaptive inertial weights and the position of mutated particles. Relying on the real-time monitoring data of rectangular pipe jacking tunnel construction on the west extension of Liuye Avenue, the improved particle swarm algorithm (IPSO) combined with the traditional BP neural network is used to establish a PSO-BP hybrid prediction model for the surface settlement of rectangular pipe jacking tunnels, and the prediction performance and stability of the old and new algorithm models are compared and analysed to provide theoretical support and technical reference for the design and construction of similar rectangular pipe jacking tunnels. In addition, improved particle swarm optimization (IPSO) has been used successfully in other fields. For example, Jin et al.The BP neural network , also known as the error backpropagation neural network, contains two learning processes of forwards transmission of input signals and backpropagation of error signals, mainly composed of an input layer, hidden layer, and output layer in structure. Its topology is shown in Fig. mentclass2pt{minimFormula:entclass1pt{minimaThe particle swarm algorithm is an evolutionary computing technique that simulates a bird in a flock by designing a massless particle with only two properties: speed and position, where speed represents how fast it moves, and position represents the direction of movement. Each particle in the search space of the optimal solution is recorded as the current individual extreme value, and the individual extreme value is shared with other particles in the entire particle group. The optimal individual extreme value is found as the current global optimal solution of the entire particle group. All particles in the particle group adjust their speed and position according to the current individual extreme value they find and the current global optimal solution shared by the entire particle group and at the same time continue to iterate until the optimal solution of the target is found. The speed and position of the particles are set as follows. The formula for updating the velocity and position of the particles is shown in Eq. :3\documeFormula: k is the number of iterations;The improved PSO-BP neural network model proposed in this paper is hereinafter collectively referred to as the IPSO-BP hybrid prediction model, and the outstanding advantage of the traditional BP neural network is that it has a strong nonlinear mapping ability and flexible network structure. The number of intermediate layers and the number of neurons in each layer can be set arbitrarily according to the specific situation, and the operation is relatively simple and convenient, but the learning rate is slow, the convergence time is long, and the random initial weights and thresholds will have a greater impact on the results, thereby reducing the prediction performance of the network as a whole. To reduce the impact, this paper adopts the improved particle swarm algorithm and introduces the linear decreasing inertia weight shown in . Throughshown in  and the entclass1pt{minimashown in  , mean squared error (MSE), and mean absolute error (MAE), such as Eqs. –10).8\d.8\d10).8yi is the true value of the data; fi is the predicted value; Formula: The number of hidden layer nodes is set according to Kolmogorov's theorem, mentclass2pt{minimAt the central pile number K7 + 081.113 in the center of the road, the west extension of Liuye Avenue is equipped with a two-hole net aperture of 18.2 × 6.0 m diagonal rectangular pipe jacking tunnel, the rectangular pipe jacking tunnel and the long-tension high-speed diagonal intersection of 63.881°, the total length of each hole rectangular pipe jacking tunnel is 51 m, and the thickness of the tunnel top cover is approximately 2.000 ~ 2.683 m. The rectangular pipe jacking tunnel is prefabricated in 4 sections, each with a length of 12 m, 13 m, 13 m, and 13 m. The tunnel construction adopts the form of setting up working pits, back walls, skateboards, and steel shield supports on site and adopts the construction process of "diagonal, diagonal, and frontal roof". The settlement joints connected by each rectangular tube jacking tunnel are 4 cm wide. The general arrangement of the rectangular pipe jacking tunnel of the west extension of Willow Avenue is shown in Fig. In the construction of this project, it is impossible to interrupt the traffic on the Changzhang Expressway, and the steel shield bracket is erected on site. A matching jack is laid in the relay room and tail end of the rectangular pipe jacking tunnel for jacking construction, but the jacking construction will disturb the highway subgrade and cause the road surface to settle and deform, which easily affects driving safety. To this end, steel shield brackets are used to maintain the slope ratio of the highway, which improves the safety and applicability of the construction, especially for tunnel jacking construction, which is more reliable and minimizes the repeated disturbance of the highway subgrade during shallow cladding jacking construction. However, the steepening rate of the rectangular pipe jacking tunnel is too fast, and the core soil is overexcavated, which will still cause the road surface to sink. In addition, when the rectangular pipe jacking tunnel comes out of the hole, due to the thin overlying soil layer (only 2 m at the thinnest point of this project), if the jacking rate is too fast, the jacking rectangular pipe jacking tunnel will drive the upper layer of overburden to move along the top direction (backsoil effect), resulting in horizontal displacement of the door frame beam at the exit opening and even causing longitudinal cracking of the road surface. The three-dimensional measurement point layout of the road surface is shown in Fig. All the data in this article are derived from the construction site monitoring of the rectangular pipe jacking tunnel on Willow Leaf Avenue. According to the research of Zhang et al., the factors affecting surface settlement can be divided into four categories: tunnel geometric parameters, excavation parameters, geological conditions and abnormal factors. Since the jacking construction is excavated at the same level, the relevant specifications and burial depth of the tunnel hardly change, and the influence of the tunnel geometry on the results is not taken into account here. In the selection of excavation parameters, the jacking force and excavation speed are mainly selected, which are the key parameters of pipe jacking construction control and can be collected in real time through sensors and other data acquisition systems, which are accurate and reliable. At the same time, it also takes into account the large influence of parameters such as grouting pressure on overcutout voids and later settlement. Therefore, several variables of the above boring parameters are the main factors affecting surface settlement. The geological conditions need to consider the basic physical and mechanical properties of the soil layer and the spatial position of the soil layer, and the soil excavated in this project is relatively homogeneous, all silty clay, so the influence of geological conditions on the results can be ignored. Since tunnel jacking construction adopts the method of manual excavation, the process is relatively simple, and there are few abnormal factors, so it is not considered for the time being. In summary, based on the important and easy-to-obtain indicators in the actual construction process, the jacking force, tunneling speed, and grouting pressure are selected as the input parameters of the prediction model. The jacking force is a major factor causing soil settlement. The jacking speed directly affects the stability of the soil layer during the construction process. As an auxiliary process in the jacking process, the grouting pressure can increase the degree of friction in the jacking process and accelerate the construction progress, and a total of 27 sets of data were used, of which approximately 75% of the first 20 groups were used for the training and calculation of the model, and the last 7 groups were used for the prediction of the test set. The specific measurement point data sets are shown in Tables As the grid is iteratively updated by backpropagation and the overall error is gradually reduced, Figs. To study the sedimentation change law of the measurement point with time, considering the influence of the jacking force, ejection speed and grouting pressure, the BP and IPSO-BP hybrid prediction model were constructed based on the monitoring data and model parameters. The test set of the corresponding measurement points on the left and right amplitudes of the rectangular pipe jacking tunnel is trained, and the prediction results and real settlement data are obtained as shown in Figs. Analysis of Figs. To study the settlement law of measurement points on the same section at the same time, the variables in the model can be regarded as constants, and the only independent variable is the distance of the measurement point from the centerline of the rectangular pipe jacking tunnel . According to the distribution of measurement points, the settlement of longitudinal measurement points is predicted and analysed, and the real settlement and predicted settlement pairs of the left and right amplitude Y4'-Z1' and Y4'-Y1' measurement points are obtained, as shown in Figs. To better compare the prediction performance of the IPSO-BP hybrid prediction model, the average value of the left and right horizontal measurement point evaluation index is mainly used for comparison, and the prediction performance is shown in Figs. The comparative analysis of Figs. A)The improved adaptive weight can make the particles dynamically adjust with the iterative process, which greatly increases the probability of finding the optimal parameters. At the same time, adding the factor of particle mutation can ensure that the particles jump out of the local optimal value in the later stage of convergence to carry out a new optimization path. Theoretically, as long as the number of iterations is large, the algorithm for optimizing the optimal threshold and weight of the BP neural network introduced in this paper must be able to find the best parameters for model prediction. The results show that the improved IPSO-BP algorithm performs better than the other comparison models in this paper.B)For transverse measuring points, the settlement prediction error of the improved IPSO-BP algorithm model is significantly smaller than that of the other three traditional algorithm models. The maximum single-point prediction error of the former is − 2 mm, the PWPSO-BP model is 6.57 mm, the AWPSO-BP model is 13.12 mm, and the BP model is 30 mm. The average prediction error of IPSO-BP is − 1.814 mm, that of PWPSO-BP is 4.28 mm, that of AWPSO-BP is 4.33 mm, and that of BP is 9.189 mm. It can be seen that the improved IPSO-BP algorithm model has great advantages over other traditional models in predicting single-point settlement.C)The improved IPSO-BP hybrid prediction model verifies that it can predict the surface settlement law during the construction process in the ultrashallow buried large-section rectangular pipe jacking tunnel project, and the results are more accurate, which can provide new ideas for settlement precontrol of similar projects. Based on the support of engineering intelligent big data, the applicability and accuracy of the model can be further improved by expanding the data set to strengthen learning.In this paper, the improved IPSO-BP algorithm with adaptive inertial weights and particle variability factors is introduced, combined with the traditional BP neural network, and the horizontal and longitudinal surface settlement data of the corresponding monitoring points are compared and predicted. The main conclusions are as follows:Because the improved IPSO-BP hybrid prediction model does not take into account all the factors affecting surface subsidence, it has certain limitations. However, the method proposed in this study has certain universality. As long as the index selection can be based on the actual situation of the project, it can also be applied to study the settlement of other pipe jacking. When the project is relatively complex and there are many indicators selected, dimension reduction work should be further carried out. The higher-dimensional data are projected into the low-dimensional space to maximize the retention of the information of the original data and finally predict the calculation. Our next research will use more popular and stable algorithms to apply to other studies to help other researchers interested in this knowledge field.


This paper investigates the manipulation of induced torque on a four-level tripod atom system through the interaction with two vortex probe beams featuring spatial inhomogeneity, along with a non-vortex control beam. The study explores both the linear and nonlinear regimes of torque induction. In the linear regime, where the intensity of the vortex beams is weaker than that of the control field, effective control over the induced torque is demonstrated by adjusting parameters such as magnetic field strength, control field intensity, detuning, and dephasing terms between relevant atomic levels. The analysis highlights the significant contribution of the Zeeman shift-induced magnetic field, which enhances the torque and exhibits a distinct sharp peak. Furthermore, higher-order contributions to the induced torque are discussed as the intensity of the probe fields approaches that of the control field, resulting in further enhancement of the induced torque. These findings offer opportunities for precise control over the rotational motion of atoms within the system, with potential applications in precision measurement, quantum information processing, and quantum control. Within the domain of multilevel atomic systems, the interplay of quantum coherence and interference gives rise to a plethora of captivating optical phenomena. These include extraordinary phenomena like lasing without the need for population inversion (LWI)17.Researchers have delved into the concept of LWI, a groundbreaking achievement in the field. In this intriguing phenomenon, the typical requirement of an inverted population of energy levels is circumvented, enabling laser emission under unconventional conditions. Enhanced index of refraction without absorption, an exciting prospect challenges traditional assumptions by allowing the manipulation of refractive properties without introducing unwanted energy loss. This breakthrough holds significant potential for various applications demanding precise control over light propagation. The STIRAP offers a powerful technique for transferring population between quantum states. By skillfully manipulating the system’s energy levels and laser pulses, one can achieve efficient and precise population transfer, opening avenues for advanced quantum control. The optical bistability and Kerr nonlinearity hold promise for shaping and manipulating light in nonlinear optical devices. By enhancing the nonlinear response of a material, novel applications in areas such as optical logic gates, all-optical switching and information processing become attainable. The EIT presents an enthralling effect where a normally opaque medium becomes transparent due to quantum interference. This phenomenon paves the way for further advancements, especially in slow light19. These beams exhibit a spiral phase characterized by the expression l. This integer, referred to as the topological charge or beam order, serves as a measure of the number of azimuthal 18. Consequently, the presence of a phase singularity at the beam’s core gives rise to a ring-shaped intensity profile, imparting a distinct visual appearance to the beam. Over the years, researchers have successfully generated optical vortices characterized by phase singularities20. These vortices are now routinely created with the ability to carry specific values of OAM, thereby enabling precise manipulation and control of the angular momentum properties of light beams21.Light beams possessing spiral phase dislocations exhibit a fascinating property known as orbital angular momentum (OAM), which stems from the peculiar phase distribution across their cross-section32. Among these effects, particular attention has been devoted to exploring the mechanical consequences of orbital angular momentum (OAM) on particles and atoms33. Notably, light beams possessing OAM have been effectively employed to induce rotation in particles confined within optical tweezers35. The transfer of OAM to matter has been shown to engender a torque linked to the transfer of wave energy39.The interaction between optical vortices and matter gives rise to a diverse array of intriguing effects, as extensively studied by researchers38. By employing counter-propagating LG and Gaussian laser beams with the same linear polarization, the BEC was effectively trapped axially, and the OAM of the LG beam induced a torque on the center of mass of each trapped atom, causing the entire medium to rotate around the beam axis and resulting in the formation of an atomic vortex state.Andersen et al. demonstrated a remarkable phenomenon involving the coherent transfer of photon OAM to a Bose-Einstein condensate (BEC) composed of sodium atoms. This was achieved through a two-photon stimulated Raman process employing Laguerre-Gaussian (LG) beams40. This torque exerted a rotational effect on the entire BEC, as the many-body wave function of the BEC could be approximated by the product of identical single-particle wave functions38. Very recently, Hamedi et al. investigated the light-induced enhanced torque on a double-V-type quantum emitter through quantum interference in spontaneous emission41. The magnitude of this induced torque was found to be controllable through the initial internal state preparation of the emitter. Additionally, they examined a fascinating scenario wherein the double-V emitter was positioned above the surface of a bismuth-chalcogenide material, while vortex beams propagated parallel to the surface. Numerical investigations were conducted to explore the consequences of spontaneous emission interference induced by the presence of bismuth-chalcogenide microstructures on the imposed torque experienced by the double-V emitters.In an alternative study, Lembessis and Babiker proposed a different scenario for inducing torque on a three-level 42. This paves the way for advancements in superconducting atomic devices and opens up possibilities for studying quantum phenomena on a macroscopic scale. Additionally, the manipulation of induced torque on tripod atoms finds utility in the field of quantum repeaters. Quantum repeaters play a crucial role in long-distance quantum communication, where the challenge lies in preserving the delicate quantum information over extended transmission distances. By utilizing the induced torque and the associated OAM-carrying photons, quantum repeaters can be designed to enhance the transmission and storage of quantum information43. This has the potential to revolutionize secure quantum communication networks by enabling efficient and robust long-distance quantum information transfer. The proposed torque on tripod atoms not only holds promise for precision measurement and quantum information processing, but also finds utility in superconducting atomic devices and quantum repeaters. These applications showcase the versatility and potential impact of the proposed torque in various fields of research and technology. With this inspiration, this paper investigates the induced torque on a four-level tripod atom system. The system is subjected to two counter-propagating, spatially inhomogeneous vortex probe beams, along with a non-vortex control beam. In the linear regime, the effective control over the system’s torque can be achieved by adjusting various factors such as magnetic field strength, intensity and detuning of the control field, and the dephasing terms between relevant atomic levels. Notably, we observe that the magnetic field resulting from the Zeeman shift of atom levels plays a crucial role in enhancing the torque. This leads to spike-like behavior in the torque as a function of probe detuning, offering precise control over the rotational motion of the atoms within the system. Such a torque is shown to further enhanced in the nonlinear regime, when the intensity of the probe fields is comparable with the control field. Our findings highlight the potential for manipulating induced torque in these systems, presenting opportunities in precision measurement, quantum information processing, and quantum control.These investigations shed light on the captivating interplay between optical vortices and matter, revealing the profound influence of OAM on the rotational dynamics of particles and atoms. The manipulation of induced torque on atoms indeed holds significant importance in specific experiments aimed at characterizing the dynamics of the ensemble. Moreover, the proposed torque on tripod atoms has promising applications in the realization of superconducting atomic devices. By harnessing the induced torque, it becomes possible to generate superflow and achieve the superposition of macroscopic states in atomic vapors40 or four-level double-V41 schemes, particularly in achieving a larger induced torque. These advantages stem from the inclusion of additional levels and transitions, which enhance the system’s interaction with external fields. Compared to the three-level 41. By incorporating these control mechanisms, our study expands the possibilities for manipulating the induced torque, enabling precise control over the rotational motion of atoms within the system. Moreover, our study goes beyond the linear regime and explores the nonlinear regime, where the intensities of the probe and control fields are comparable and exhibit strong interactions. In contrast, the analysis conducted in the previous study41 focused solely on the weak light-matter interaction regime. By considering the nonlinear regime, our study enhances the control over the system’s rotational motion, which proves advantageous for applications such as precision measurement or quantum information processing.The four-level tripod scheme presents clear advantages over the three-level 44. Additionally, it facilitates the occurrence of polarization phase gates, which have significant implications in various applications45.We investigate a tripod system that includes an excited level and three lower levels. The system interacts with two weak probe lasers that have equal Rabi frequencies, mentclass2pt{minimmentclass2pt{minimmentclass2pt{minimAssuming the dipole approximation and the rotating wave approximation, the tripod Hamiltonian can be expressed in the interaction picture as follows:r, the topological (OAM) number is represented by an integer l, and the azimuthal angle is given by The electric dipole approximation allows the expression of the vortex beams 41. The scattering force acting on the center-of-mass coordinate Assuming that the trapped atoms can be described by identical single-particle wave functions , the many-body wave function can be approximated as their product. In this case, each atom experiences the same force due to the light, and the single-body wave function can be expressed as a product of internal and center-of-mass wave functionsUsing Eqs. –6) one  one 6) oTo provide a complete understanding of the dynamics of the four-level tripod model, we use the Bloch equations for the density matrix elements, which take into account atomic spontaneous emission and dephasing.Here, The steady-state solutions to the optical Bloch equations are considered in our tripod model. When the intensity of the control field is stronger than the intensity of both probe fields, the stationary population distribution is symmetric with respect to 41. For the simulations in the linear regime in this paper, we assume B on the induced torque, we introduce a scaling factor 46. This scaling enables us to examine the effects of varying magnetic field strengths without altering the other system parameters.The torque function given in Eq.  is depen45.We begin by investigating the influence of magnetic field on the induced torque experienced by tripod atoms, and explore the potential for using this parameter to control the torque. Figure When the magnetic field is turned on Fig. b, the diIn addition to the influence of the magnetic field on the variation of induced torque on tripod atoms, we also investigate the impact of control field intensity and detuning on the torque. Figure B. Hence, adjusting the Zeeman shift of atom levels, which is induced in the presence of a magnetic field, can efficiently manipulate the torque, allowing for enhanced torque at specific frequency detunings while achieving vanishing torque at other detunings. This observation suggests a simple and efficient way to control the rotational motion of a tripod atom system. By modulating the magnetic field, it is possible to manipulate the atom’s magnetic moment and its coupling to the external fields, thereby affecting the induced torque. In addition, the dephasing terms lead to a manipulation of the resonance peaks and a modification of the torque shape. This implies that the torque can be tuned to respond differently to changes in the probe detuning and the control field intensity or detuning, allowing for greater control of the system’s rotational motion. This highlights the potential of using magnetic fields to manipulate the rotational dynamics of tripod atom systems, which could have significant implications for applications in areas such as quantum information processing and precision sensing. In what follows we explore a more complex condition (when nonlinearities are present) to maximize the torque and achieve greater control over the system’s motion.The magnitude of torque can be significantly enhanced when the dephasing terms are non-zero, as shown in Fig. The expressions for the density matrix elements,  in Eqs. , 22) we we\docum45However, in this section, we investigate the behavior of the induced torque on the tripod atoms when the probe pulses are not necessarily weak. To achieve this, we derive general expressions for the steady-state probe field density matrix elements,  in Eqs. , 22). H. H\documIn this section, we consider a scenario where the intensity of the probe fields is strong enough to be comparable with the intensity of the control fields. Specifically, we set Figure In conclusion, this study delved into the manipulation of induced torque in a four-level tripod atom system interacting with two vortex probe beams and a non-vortex control beam. In the linear regime when the probe vortex beams are much weaker than the contol field, we demonstrated effective control over the induced torque by fine-tuning parameters such as magnetic field strength, control field intensity, detuning, and dephasing terms. Our analysis highlighted the significant role of the Zeeman shift-induced magnetic field, which not only enhanced the torque but also exhibited a distinct peak behavior concerning probe detuning, allowing for the precise control of rotational motion within the system. Furthermore, our investigation extended to the nonlinear regime, where the intensity of the probe fields approached that of the control field. In this regime, we observed higher-order contributions to the induced torque, leading to further enhancement of the light induced torque. This opens up possibilities for even greater control and manipulation of atomic rotational dynamics. The ability to manipulate induced torque in tripod atoms systems holds promise for a range of applications, including precision measurement, quantum information processing, and quantum control. With improved control over rotational motion, we can explore advanced technologies and harness the potential of these systems for future advancements.


With the development of urban road traffic, road noise pollution is becoming a public concern. Controlling and reducing the harm caused by traffic noise pollution have been the hot spots of traffic noise management research. The subjective annoyance level of traffic noise has become one of the most important measurements for evaluating road traffic pollution. There are subjective experimental methods and objective prediction methods to assess the annoyance level of traffic noise: the subjective experimental method usually uses social surveys or listening experiments in laboratories to directly assess the subjective annoyance level, which is highly reliable, but often requires a lot of time and effort. The objective method extracts acoustic features and predicts the annoyance level through model mapping. Combining the above two methods, this paper proposes a deep learning model-based objective annoyance evaluation method, which directly constructs the mapping between the noise and annoyance level based on the listening experimental results and realizes the rapid evaluation of the noise annoyance level. The experimental results show that this method has reduced the mean absolute error by 30% more than the regression algorithm and neural network, while its performance is insufficient in the annoyance interval where samples are lacking. To solve this problem, the algorithm adopts transfer learning to further improve the robustness with a 30% mean absolute error reduction and a 5% improvement in the correlation coefficient between the true results and predicted results. Although the model trained on college students’ data has some limitations, it is still a useful attempt to apply deep learning to noise assessment. With the development of urban road traffic, traffic noise pollution has become an increasing public concern , which mThe current methods of noise annoyance assessment are mainly subjective and objective: subjective tests usually require social surveys or listening tests ,10, and Due to the development of artificial intelligence technology, many scholars have also tried to use neural network algorithms for a noise annoyance assessment. Jesus  used deeThis paper follows the idea of objective assessment and proposes a model for the evaluation of road traffic noise annoyance with the deep learning model. The model is based on the results of subjective listening experiments and directly constructs the mapping relationship between noise fragments and annoyance, without extracting additional acoustic features, which can achieve a fast evaluation of noise annoyance. However, due to the long data acquisition period of subjective listening experiments, the available sample size is small at this stage, and problems such as over-fitting easily occur in the training process . To avoiBased on the experimental results of this paper, the method using deep learning has reduced the mean absolute error of the prediction by about 30% more than traditional machine learning algorithms such as the regression algorithms and neural network; however, the performance is still poor in the annoyance interval with few samples. The transfer learning strategy can effectively moderate the over-fitting phenomenon, such as bad performance in few-samples intervals, but performs well in large-samples intervals , which gThe audio data for the listening experiment came from two parts: one from the SoundIdealGeneral6000 sound library  resampleThe recorded data were separated into 8 s clips. The average loudness of the audio data was between 55 phon and 90 phon. Due to the lack of a motorcycle sound, whistle road sound, road sound on rainy days, and so on, we selected the appropriate data from the sound library. A total of 949 audio data were collected.The experiments were conducted in an audiometric room  with the uneven distribution of subjects, the elder should be considered. Due to the lack of annoyance data about old people, this will lead to changes in the subjective annoyance value. (2) The number of subjects: referring to documents ,29,30, 2In the listening experiment, each noise sample was played three times at random. If the difference in annoyance given by the same subject for any two of the three evaluations of the same noise sample was 2 or more, the evaluation sample would be regarded as misjudged and needed to be removed. If the number of one subject’s misjudged samples reached 30% or more of the total sample size, the experimental results of that subject needed to be removed.After removing the invalid data, the results of the three scoring sessions of the noisy samples were averaged as the subject’s perceived annoyance with the noise samples. If the number of perceived annoyance assessments for a noise sample was less than 14, the noise sample needed to be excluded.N is the window length minus 1:After eliminating the invalid samples, the mean annoyance (MA) of the noise samples was calculated as the model label. In this paper, we selected the amplitude spectrum as the model input. The amplitude spectrum can directly provide feedback on the characteristic information of the noise; it included the amplitude value of the noise at different frequencies. To obtain the amplitude spectrum, we should conduct an STFT  on noise first, and then calculate the modulus of STFT results to obtain the amplitude spectrum. The process of STFT as a nonlinear transformation and formula is as follows:Due to calculating the mean value of annoyance, the annoyance value is no longer an integer type, so this paper divided the annoyance range [0–10] into different annoyance intervals with an interval size of 1.949 samples obtained, and the distribution of the samples is shown in Deep learning is a data-driven approach; therefore, training on a small dataset can lead to over-fitting. To avoid the bad effects of inadequate listening experimental samples on the deep learning model, this paper used transfer learning to extract the features of numerous samples. Due to the correlation between the psychoacoustic annoyance level of noise and the perceived noise annoyance , we builCommon approaches to transfer learning in deep learning are overall parameter optimization based on pre-trained models ,37 and pWith the development of deep learning, a large number of researchers have tried to use deep learning on their own tasks, and many researchers have successfully applied deep learning algorithms in many of the fields such as image segmentation , speech In this paper, we use UNet as the basic model and the reasons are as follows: (1) UNet and its variant’s version have been used in many research areas with good results, and it can provide ideas for our research ,44,45,47The encoder uses a two-dimensional convolution operation with a convolution kernel of The convolutional downsampling module realizes the downsampling function of information through a two-dimensional convolution operation and maximum pooling operation to facilitate the acquisition of detailed information of data. The convolutional downsampling will output the feature extraction result and downsampling result, the operation process of the left and right channel is the same, but the convolutional parameters are not shared: by taking one of the ways as an example, you can note the output of The shape of The feature information obtained through convolutional downsampling interacts with other features through the concatenating operation, and the fusion of the two-feature information is realized through the feature fusion module to provide input for the decoder. The input of the left path is noted as The output after the feature fusion module is sent to the decoder to calculate the final result. To better correspond the model output to the annoyance value, unlike the previous use of fully connected operations to map features from high to low dimensions, this paper performs separate dimensional reduction operations in different dimensions to take into account the different feature dimensions. The final output of the model is as follows:As shown in If the model does not decrease in loss value after 10 iterations of updates, the training is withdrawn early. The model parameter with the lowest loss in the validation set is saved as the final training result.Generally, for machine learning and deep learning datasets, the number of training sets is 70–80% of the total samples, and the number of samples in the validation and test sets is 20–30% of the total samples ,51. TakeThe psychoacoustic annoyance ranges from 0 to 100, and the model output will multiply a number to suit the range. In this paper, the best number is 80 according to the training experience. The complexity of the pre-training model is measured by the number of floating point operations, and it is 5.7 GMac. The number of hyperparameters is 0.203 M. The model with the lowest loss in the validation set is selected as the pre-training model. In this paper, we select mean absolute error (MAE) as an evaluation standard. The MAE can visually provide feedback on the average error between the predicted result and the real result, the evaluation weight for each error is equal, it is less affected by anomaly samples , and it At the formal training stage, based on the pre-trained model, the model parameters will be optimized on the listening experiment dataset , Table 1This section performs a secondary optimization of the overall parameters and the model’s decoder parameters, respectively. The two methods will be denoted as total-tuning and fine-tuning. The training dataset is the listening experiment dataset with the total. Due to the different value ranges, in the secondary optimization, the model output will multiply eight by the empirical to suit the range. To compare the performance of the algorithms, artificial neural networks , linear The evaluation accuracy of each type of algorithm on different annoyance intervals is shown in A comparison of the evaluated performance of each algorithm on different annoyance intervals is shown in In this paper, a deep learning-based objective assessment method for road traffic noise annoyance is proposed. Different from  and 17]17], thisThis paper adopts three strategies to train the deep learning network, one of which is directly trained on the listening experiment dataset with better performance than commonly used machine learning algorithms. The other two, called total-tuning and fine-tuning, respectively, are pre-trained on the psychoacoustic dataset first and then trained on the listening experiment dataset, where total-tuning will optimize all parameters and fine-tuning just optimizes the parameter of the decoder: this is common in transfer learning to solve the problem of an insufficient dataset and model over-fitting ,37,38. TThis paper proposes a deep learning-based objective assessment method for road traffic noise annoyance that can achieve a rapid assessment of road traffic noise annoyance using the amplitude spectrum of traffic noise without incorporating subjects’ personal information or additional acoustic features. To obtain the dataset required for training the model, the authors conduct listening experiments to gather reliable raw data, from which abnormal samples are removed to construct a dataset of the perceived annoyance of road traffic noise.To evaluate the reliability of the algorithms, artificial neural networks, linear regression based on a principal component analysis, Ridge regression, and Lasso regression are used for comparison. The experimental results show that the evaluation results obtained using deep learning models produce optimal results for the mean absolute error, Pearson’s correlation coefficient, and Spearman’s correlation coefficient. However, in terms of the prediction accuracy in different annoyance intervals, the difference between the maximum and minimum evaluation errors in different intervals for the directly trained model is 1.21, which is less robust. To address this issue, the paper introduces transfer learning to optimize the algorithm through a computer-generated psychoacoustic annoyance degree dataset, preliminary optimization of model parameters to obtain a pre-trained model, and secondary optimization of overall parameters and some parameters on the perceptual noise annoyance degree dataset.The experimental results show that the model with partial parameter optimization shows a significant improvement in each measure and its accuracy for annoyance intervals with a small sample size has significantly improved. Last but not the least, this study is only conducted for college students, which may lead to certain limitations in the final performance, but this method is still a useful attempt to combine deep learning with noise evaluation. The evaluation results obtained by the deep learning model can be an effective evaluation reference for urban planning, noise management, and relevant noise policy formulation.