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Review of Fault Diagnosis Techniques for UAV Flight Control Systems AN Xue, LI Shaobo, ZHANG Yizong, ZHANG Ansi Computer Engineering and Applications    2023, 59 (24): 1-15.   DOI: 10.3778/j.issn.1002-8331.2305-0137 Abstract （279）      PDF（pc） （917KB）（749）       Knowledge map    Save In recent years, unmanned aerial vehicles（UAVs） have been widely used in various complex fields of military and civilian applications due to their unique advantages such as low operating costs and high mobility. At the same time, the complex and diverse missions have put forward higher requirements for the reliability and safety of UAV systems. The UAV fault diagnosis technology can provide timely and accurate diagnosis results, which helps the maintenance, repair and servicing of UAVs, and is of great significance in enhancing the combat effectiveness of UAVs. Therefore, this paper firstly analyses UAV flight control systems, and classifies the faults. Secondly, the research methods and status quo of UAV fault diagnosis technology are analysed and summarised. Finally, the main challenges faced by UAV fault diagnosis technology are discussed and the future development direction is pointed out; the aim is to provide some reference for researchers in the field of UAV fault diagnosis technology and to promote the improvement of UAV fault diagnosis technology level in China. Reference | Related Articles | Metrics

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Research Progress of YOLO Series Target Detection Algorithms WANG Linyi, BAI Jing, LI Wenjing, JIANG Jinzhe Computer Engineering and Applications    2023, 59 (14): 15-29.   DOI: 10.3778/j.issn.1002-8331.2301-0081 Abstract （949）      PDF（pc） （1009KB）（569）       Knowledge map    Save The YOLO-based algorithm is one of the hot research directions in target detection. In recent years, with the continuous proposition of YOLO series algorithms and their improved models, the YOLO-based algorithm has achieved excellent results in the field of target detection and has been widely used in various fields in reality. This article first introduces the typical datasets and evaluation index for target detection and reviews the overall YOLO framework and the development of the target detection algorithm of YOLOv1~YOLOv7. Then, models and their performance are summarized across eight improvement directions, such as data augmentation, lightweight network construction, and IOU loss optimization, at the three stages of input, feature extraction, and prediction. Afterwards, the application fields of YOLO algorithm are introduced. Finally, combined with the actual problems of target detection, it summarizes and prospects the development direction of the YOLO-based algorithm. Reference | Related Articles | Metrics

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Survey of Transformer-Based Object Detection Algorithms LI Jian, DU Jianqiang, ZHU Yanchen, GUO Yongkun Computer Engineering and Applications    2023, 59 (10): 48-64.   DOI: 10.3778/j.issn.1002-8331.2211-0133 Abstract （984）      PDF（pc） （875KB）（561）       Knowledge map    Save Transformer is a kind of deep learning framework with strong modeling and parallel computing capabilities. At present, object detection algorithm based on Transformer has become a hotspot. In order to further explore new ideas and directions, this paper summarizes the existing object detection algorithm based on Transformer as well as a variety of object detection data sets and their application scenarios. This paper describes the correlation algorithms for Transformer based object detection from four aspects, i.e. feature extraction, object estimation, label matching policy and application of algorithm, compares the Transformer algorithm with the object detection algorithm based on convolutional neural network, analyzes the advantages and disadvantages of Transformer in object detection task, and proposes a general framework for Transformer based object detection model. Finally, the prospect of development trend of Transformer in the field of object detection is put forward. Reference | Related Articles | Metrics

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Review of Research on Road Traffic Flow Data Prediciton Methods MENG Chuang, WANG Hui, LIN Hao, LI Kecen, WANG Xinpeng Computer Engineering and Applications    2023, 59 (14): 51-61.   DOI: 10.3778/j.issn.1002-8331.2209-0458 Abstract （935）      PDF（pc） （605KB）（442）       Knowledge map    Save As an important branch of intelligent transportation system, road traffic flow prediction plays an important role in congestion prediction, path planning. The spatio-temporal polymorphism and complex correlation of road traffic flow data force the transformation and upgrading of road traffic flow prediction methods in the era of big data. In order to mine the time-space characteristics of traffic flow, scholars have proposed various methods, including model fusion, model algorithm improvement, data definition conversion, etc, in order to improve the prediction accuracy of the model. In order to reasonably summarize all kinds of traffic flow prediction methods, they are divided into three categories according to the types of methods used：statistics based methods, machine learning based methods, and depth learning based methods. This paper summarizes and analyzes the new models and algorithms in recent years by summarizing various traffic flow prediction methods, aiming to provide research ideas for relevant researchers. Finally, the methods of traffic flow prediction are summarized and prospected, and the exploration direction of the future traffic flow prediction field is given. Reference | Related Articles | Metrics

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Review of Explainable Artificial Intelligence ZHAO Yanyu, ZHAO Xiaoyong, WANG Lei, WANG Ningning Computer Engineering and Applications    2023, 59 (14): 1-14.   DOI: 10.3778/j.issn.1002-8331.2208-0322 Abstract （671）      PDF（pc） （683KB）（435）       Knowledge map    Save With the development of machine learning and deep learning, artificial intelligence technology has been gradually applied in various fields. However, one of the biggest drawbacks of adopting AI is its inability to explain the basis for predictions. The black-box nature of the models makes it impossible for humans to truly trust them yet in mission-critical application scenarios such as healthcare, finance, and autonomous driving, thus limiting the grounded application of AI in these areas. Driving the development of explainable artificial intelligence（XAI） has become an important issue for achieving mission-critical applications on the ground. At present, there is still a lack of research reviews on XAI in related fields at home and abroad, as well as a lack of studies focusing on causal explanation methods and the evaluation of explainable methods. Therefore, this study firstly starts from the characteristics of explanatory methods and divides the main explainable methods into three categories：model-independent methods, model-dependent methods, and causal explanation methods from the perspective of explanation types, and summarizes and analyzes them respectively, then summarizes the evaluation of explanation methods, lists the applications of explainable AI, and finally discusses the current problems of explainability and provides an outlook. Reference | Related Articles | Metrics

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Construction and Application of Discipline Knowledge Graph in Personalized Learning ZHAO Yubo, ZHANG Liping, YAN Sheng, HOU Min, GAO Mao Computer Engineering and Applications    2023, 59 (10): 1-21.   DOI: 10.3778/j.issn.1002-8331.2209-0345 Abstract （654）      PDF（pc） （929KB）（425）       Knowledge map    Save The discipline knowledge graph is an important tool to support teaching activities based on big data, artificial intelligence and other technologies. As a kind of discipline knowledge semantic network, it contributes to the development of personalized learning systems and the promotion of new infrastructure for digital education resources. Firstly, this paper outlines the concept and classification of knowledge graph. Secondly, this paper summarizes the concept, characteristics, advantages, connotation and the support for personalized learning of discipline knowledge graph. Nextly, this paper focuses on the sorting of construction process of discipline knowledge graph：discipline ontology construction, discipline knowledge extraction, discipline knowledge fusion and discipline knowledge processing, and it also introduces the application of discipline knowledge graph in personalized learning situations and the challenges. Finally, this paper prospects the future tendency of discipline knowledge graph and personalized learning. It provides the reference and inspiration for the organization of educational resources and the innovative development of personalized learning. Reference | Related Articles | Metrics

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Image Inpainting Algorithm Based on Deep Neural Networks LYU Jianfeng, SHAO Lizhen, LEI Xuemei Computer Engineering and Applications    2023, 59 (20): 1-12.   DOI: 10.3778/j.issn.1002-8331.2303-0111 Abstract （375）      PDF（pc） （720KB）（389）       Knowledge map    Save With the rapid development of deep learning, computer vision technology is applied more and more widely. At the same time, the image inpainting technology based on the known information of the damaged image using deep neural network has also become a hot topic. The image inpainting methods based on depth neural network in recent years are reviewed and analyzed. Firstly, the image inpainting methods are classified and summarized according to the view of model optimization. Then the common datasets and performance evaluation indicators are introduced, and the performance evaluation and analysis of various deep neural network-based image inpainting algorithms are carried out on the relevant data sets. Finally, the challenges faced by the existing image inpainting methods are analyzed, and the future research works are prospected. Reference | Related Articles | Metrics

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Multi-Modal Meteorological Forecasting Based on Transformer XIANG Deping, ZHANG Pu, XIANG Shiming, PAN Chunhong Computer Engineering and Applications    2023, 59 (10): 94-103.   DOI: 10.3778/j.issn.1002-8331.2208-0486 Abstract （567）      PDF（pc） （977KB）（387）       Knowledge map    Save Thanks to the rapid development of meteorological observation technology, the meteorological industry has accumulated massive meteorological data, which provides an opportunity to build new data-driven meteorological forecasting methods. Due to the long-term dependence and large-scale spatial correlation hidden in meteorological data, and due to the complex coupling relationship between different modalities, meteorological forecasting with deep learning is still a challenging research topic. This paper presents a deep learning model for meteorological forecasting based on multi-modal fusion, using sequential multi-modal data in same atmospheric pressure levels composed of four classical meteorological elements：temperature, relative humidity, U-component of wind and V-component of wind. Specifically, convolutional network is used to learn features from every modality, and with those features, the gating mechanism is introduced to multi-modal weighted fusion. Secondly, the attention mechanism is introduced, which replaces the traditional attention mechanism with parallel spatial-temporal axial attention, in order to effectively learn long-term dependencies and large-scale spatial associations. Architecturally, the Transformer encoder-decoder structure is employed as the overall framework. Extensive comparative experiments have been conducted on the regional ERA5 reanalysis dataset, demonstrating that the proposed method is effective and superior in the prediction of temperature, relative humidity and wind. Reference | Related Articles | Metrics

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Review on Research and Application of Deep Learning-Based Target Detection Algorithms ZHANG Yangting, HUANG Deqi, WANG Dongwei, HE Jiajia Computer Engineering and Applications    2023, 59 (18): 1-13.   DOI: 10.3778/j.issn.1002-8331.2305-0310 Abstract （628）      PDF（pc） （662KB）（386）       Knowledge map    Save With the continuous development of deep learning, deep convolutional neural networks are increasingly used in the field of target detection and are now applied in many fields such as agriculture, transportation, and medicine. Compared with traditional feature-based manual methods, deep learning-based target detection methods can learn both low-level and high-level image features with better detection accuracy and generalization ability. To outline and summarize the latest advances and technologies in the field of target detection, the status of deep learning-based target detection algorithms and applications is reviewed by analyzing the deep learning-based target detection technologies in recent years. Firstly, the development, advantages and disadvantages of two kinds of target detection network architectures, two phases and single phase, are summarized; secondly, the backbone network, data set and evaluation metrics are described, the detection accuracy of classical algorithms are compared, and the improvement strategies of classical target detection algorithms are summarized; finally, the current stage of target detection applications are discussed, and future research priorities in the field of target detection are proposed. Reference | Related Articles | Metrics

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Review of Object Detection Algorithm Improvement in Deep Learning YANG Feng, DING Zhitong, XING Mengmeng, DING Bo Computer Engineering and Applications    2023, 59 (11): 1-15.   DOI: 10.3778/j.issn.1002-8331.2209-0312 Abstract （470）      PDF（pc） （691KB）（333）       Knowledge map    Save Object detection is currently a research hotspot in the field of computer vision. With the development of deep learning, object detection algorithms based on deep learning are increasingly applied and their performance is constantly improved. This paper summarizes the latest research progress of object detection methods based on deep learning by summarizing common problems encountered in the process of object detection and corresponding improvement methods. This paper focuses on two types of object detection algorithms based on deep learning. In addition, the latest improvement ideas of target detection algorithms are summarized from the aspects of attention mechanism, lightweight network, multi-scale detection. Finally, in view of the current problems in the field of target detection, the future development trend is prospected. And the feasible solution is put forward in order to provide reference ideas and directions for the follow-up research work in this field. Reference | Related Articles | Metrics

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Survey on Credit Card Transaction Fraud Detection Based on Machine Learning JIANG Hongxun, JIANG Junyi, LIANG Xun Computer Engineering and Applications    2023, 59 (21): 1-25.   DOI: 10.3778/j.issn.1002-8331.2302-0129 Abstract （445）      PDF（pc） （674KB）（293）       Knowledge map    Save Machine learning has its distinctiveness in credit card transaction detection and faces a more complex environment. Since the intervention of human intelligence, machine learning encounters harder challenges in fraud detection than the ones of face recognition and driverlessness, which leads to failures if only applying the processes of engineering disciplines. This paper depicts the 2000-since research history of credit card anti-fraud; identifies the definition, scope, technical streams, applications, and other key concepts, and their interconnections in the field of detection oriented machine learning; analyzes the general architecture of fraud detection and summarizes the state-of-the-art of transaction fraud detection research in terms of feature engineering, models/algorithms, and evaluation metrics; discusses various detection algorithms of credit card transaction fraud and enumerates their original intention, core ideas, solution methods, advantages or disadvantages, and relevant extensions; highlights unsupervised, supervised, and semi-supervised learning models of fraud recognition, as well as various ensembles such as models cascading and aggregation; addresses three major challenges, i.e., massive data, sample skew, and concept drift, and compiles the latest progresses to alleviate these problems. This paper concludes with the limitations, controversies, and challenges of machine learning on credit card fraud recognition, and provides the trend analysis and suggestions for future research directions. Reference | Related Articles | Metrics

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Survey of Sentiment Analysis Algorithms Based on Multimodal Fusion GUO Xu, Mairidan Wushouer, Gulanbaier Tuerhong Computer Engineering and Applications    2024, 60 (2): 1-18.   DOI: 10.3778/j.issn.1002-8331.2305-0439 Abstract （394）      PDF（pc） （954KB）（284）       Knowledge map    Save Sentiment analysis is an emerging technology that aims to explore people’s attitudes toward entities and can be applied to various domains and scenarios, such as product evaluation analysis, public opinion analysis, mental health analysis and risk assessment. Traditional sentiment analysis models focus on text content, yet some special forms of expression, such as sarcasm and hyperbole, are difficult to detect through text. As technology continues to advance, people can now express their opinions and feelings through multiple channels such as audio, images and videos, so sentiment analysis is shifting to multimodality, which brings new opportunities for sentiment analysis. Multimodal sentiment analysis contains rich visual and auditory information in addition to textual information, and the implied sentiment polarity (positive, neutral, negative) can be inferred more accurately using fusion analysis. The main challenge of multimodal sentiment analysis is the integration of cross-modal sentiment information; therefore, this paper focuses on the framework and characteristics of different fusion methods and describes the popular fusion algorithms in recent years, and discusses the current multimodal sentiment analysis in small sample scenarios, in addition to the current development status, common datasets, feature extraction algorithms, application areas and challenges. It is expected that this review will help researchers understand the current state of research in the field of multimodal sentiment analysis and be inspired to develop more effective models. Reference | Related Articles | Metrics

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Survey of Fully Cooperative Multi-Agent Deep Reinforcement Learning ZHAO Liyang, CHANG Tianqing, CHU Kaixuan, GUO Libin, ZHANG Lei Computer Engineering and Applications    2023, 59 (12): 14-27.   DOI: 10.3778/j.issn.1002-8331.2209-0186 Abstract （412）      PDF（pc） （661KB）（282）       Knowledge map    Save As one of the important branches in the field of machine learning and artificial intelligence, fully cooperative multi-agent deep reinforcement learning effectively combines the expression and decision-making ability of deep reinforcement learning with the distributed cooperation ability of multi-agent system in a general way, which provides an end-to-end solution to the model-free sequential decision-making problem in fully cooperative multi-agent system. Firstly, the basic principles of deep reinforcement learning are described, and the development of single agent deep reinforcement learning is summarized from three main directions：value function based, policy gradient based and actor-critic based. Secondly, the main challenges and training framework of multi-agent deep reinforcement learning are analyzed. Then, according to the different ways of realizing the maximum team joint reward, the fully cooperative multi-agent deep reinforcement learning is divided into four categories：independent learning, communication learning, collaborative learning and reward function shaping. Finally, from the perspective of solving practical problems, the future development direction of fully cooperative multi-agent deep reinforcement learning algorithm is prospected. Reference | Related Articles | Metrics

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Review of SLAM Based on Lidar LIU Mingzhe, XU Guanghui, TANG Tang, QIAN Xiaojian, GENG Ming Computer Engineering and Applications    2024, 60 (1): 1-14.   DOI: 10.3778/j.issn.1002-8331.2308-0455 Abstract （450）      PDF（pc） （854KB）（280）       Knowledge map    Save Simultaneous localization and mapping (SLAM) is a crucial technology for autonomous mobile robots and autonomous driving systems, with a laser scanner (also known as lidar) playing a vital role as a supporting sensor for SLAM algorithms. This article provides a comprehensive review of lidar-based SLAM algorithms. Firstly, it introduces the overall framework of lidar-based SLAM, providing detailed explanations of the functions of the front-end odometry, back-end optimization, loop closure detection, and map building modules, along with a summary of the algorithms used. Secondly, it presents descriptions and summaries of representative open-source algorithms in a sequential order of 2D to 3D and single-sensor to multi-sensor fusion. Additionally, it discusses commonly used open-source datasets, precision evaluation metrics, and evaluation tools. Lastly, it offers an outlook on the development trends of lidar-based SLAM technology from four dimensions: deep learning, multi-sensor fusion, multi-robot collaboration, and robustness research. Reference | Related Articles | Metrics

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Survey on Computational Approaches for Drug-Target Interaction Prediction ZHANG Ran, WANG Xuezhi, WANG Jiajia, MENG Zhen Computer Engineering and Applications    2023, 59 (12): 1-13.   DOI: 10.3778/j.issn.1002-8331.2210-0108 Abstract （384）      PDF（pc） （675KB）（271）       Knowledge map    Save Drug-target interaction prediction aims to discover potential drugs acting on specific proteins, and plays an important role in drug?repositioning, drug side effect prediction, polypharmacology and drug resistance research. With the advancement of computer processing and the continuous updating of computing algorithms, the computational drug-target interaction prediction has shown the advantages of short time, low cost, high precision and wide range, which has received extensive attention and made remarkable progress. In order to sort out the development history and explore the future research direction, the background and significance of drug-target interaction prediction are firstly introduced in brief. Secondly, the methods are classified into four types：molecular docking-based, drug structure-based, text mining-based and chemogenomic-based methods. A comparative analysis of each method is carried out, and the data requirements and application scenarios for each type of methods are described in detail. Finally, the limitations and challenges of the existing research are discussed, and the future research directions are prospected to provide references for follow-up research. Reference | Related Articles | Metrics

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Improved YOLOv5 for Small Object Detection Algorithm YU Jun, JIA Yinshan Computer Engineering and Applications    2023, 59 (12): 201-207.   DOI: 10.3778/j.issn.1002-8331.2302-0157 Abstract （358）      PDF（pc） （566KB）（261）       Knowledge map    Save Although the current deep learning technology has made amazing progress in the field of large and medium object detection, small object detection is still a challenging problem today due to the limited size of small object and the limitations of convolutional networks. Based on You Only Look Once version 5（hereinafter referred to as YOLOv5） algorithm, this research proposes a YOLO-S model, which is very friendly to small objects. Firstly, on the basis of the orginal output layer with only three layers, a special output layer for small object detection is added by using the cascade network. Secondly, in order to supplement context information and suppress multi-scale feature fusion conflicts, a new supplement context information module CFM and channel and spatial feature thinning module FSM is designed. Finally, the upsampling method is replaced by deconvolution from the original linear interpolation. The dataset uses VisDrone2019, which is specially designed for small objects, to verify the effectiveness of the algorithm. The experimental results show that the mAP@0.5 of YOLO-S is 6.9 percentage points higher than that of YOLOv5. Reference | Related Articles | Metrics

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Review of Single-Image 3D Face Reconstruction Methods WANG Jingting, LI Huibin Computer Engineering and Applications    2023, 59 (17): 1-21.   DOI: 10.3778/j.issn.1002-8331.2210-0041 Abstract （276）      PDF（pc） （961KB）（258）       Knowledge map    Save In recent years, 3D face reconstruction task, as an important part of “digital human” technology, has received great attention from both academia and industry. In particular, 3D face reconstruction task based on a single image has made great progress by fully combining traditional camera model, illumination model, 3D face statistical deformation model with the deep convolutional neural network and deep generative models. This paper focuses on the single-image 3D face reconstruction problem, and divides the existing research works into two categories based on implicit space coding and explicit space regression. The first type of research works optimize the basis coefficient solution and loss function design of the basic 3D face statistical model to improve the reconstruction effect, which has the advantage of robustness in face topology change but lacks detailed features. The second type of research works represent 3D faces in the forms of multiple data in explicit space and regress them directly by deep networks, which can usually obtain more personalized 3D face detail features and have better robustness to interference factors such as illumination and occlusion. Furthermore, based on the commonly used datasets and evaluation metrics, this paper fully explores and compares the advantages and disadvantages of some typical methods of both categories. Finally, it summarizes the whole paper and points out the main challenges and future development trends of the single-image based 3D face reconstruction task. Reference | Related Articles | Metrics

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Improved YOLOv7-tiny’s Object Detection Lightweight Model LIU Haohan, FAN Yiming, HE Huaiqing, HUI Kanghua Computer Engineering and Applications    2023, 59 (14): 166-175.   DOI: 10.3778/j.issn.1002-8331.2302-0115 Abstract （683）      PDF（pc） （830KB）（252）       Knowledge map    Save At present, the object detection algorithm has a large number of parameters and high computational complexity. However, the storage capacity and computing power of mobile terminals are limited and it is difficult to deploy it. So in this paper, it proposes the improved YOLOv7-tiny for mobile terminal devices. An efficient backbone network and a lightweight feature fusion network are further proposed with the ShuffleNet v1-improved and EALN-GS as the basic building units. The combination of the two part can reduce computational complexity, obtain more rich semantic information, and further improve detection accuracy. The Mish activation function is used to increase nonlinear expression and improve the generalization ability of the model. Experimental results show that compared with the original model, the accuracy of the improved model is improved by 3.3%, the number of parameters and calculations are reduced by 4.8% and 13.7%, and the model scale is reduced by 8.7%. The improved YOLOv7-tiny reduces the amount of parameters and calculations of the model while maintaining high accuracy, further improves the detection effect, and provides feasibility for deployment in edge terminal devices. Reference | Related Articles | Metrics

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Review of Deep Learning Methods for MRI Reconstruction DENG Gewen, WEI Guohui, MA Zhiqing Computer Engineering and Applications    2023, 59 (20): 67-76.   DOI: 10.3778/j.issn.1002-8331.2302-0057 Abstract （417）      PDF（pc） （580KB）（246）       Knowledge map    Save Magnetic resonance imaging（MRI） is a commonly used imaging technique in the clinic, but the excessive imaging time limits its further development. Image reconstruction from undersampled k-space data has been an important part of accelerating MRI imaging. In recent years, deep learning has shown great potential in MRI reconstruction, and its reconstruction results and efficiency are better than traditional compressed sensing methods. To sort out and summarize the current deep learning-based MRI reconstruction methods, it firstly introduces the definition of MRI reconstruction problem, secondly analyzes the application of deep learning in data-driven end-to-end reconstruction and model-driven unrolled optimization reconstruction, then provides evaluation metrics and common datasets for reconstruction, and finally discusses the challenges faced by current MRI reconstruction and future research directions. Reference | Related Articles | Metrics

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Review of Small Object Detection Algorithms Based on Deep Learning DONG Gang, XIE Weicheng, HUANG Xiaolong, QIAO Yitian, MAO Qian Computer Engineering and Applications    2023, 59 (11): 16-27.   DOI: 10.3778/j.issn.1002-8331.2211-0377 Abstract （391）      PDF（pc） （646KB）（243）       Knowledge map    Save The existing object detection algorithms have high accuracy for the detection of large objects and medium objects, but due to the few pixels in the image and the available features of small objects, the detection accuracy of small objects is too low compared with that of large objects. By fusing the feature layer, the detection of small objects has achieved good results, but there are still problems such as the localization of small objects. Based on this, the definition of small objects is first explained, and five reasons for the low detection accuracy of small objects are pointed out. Subsequently, the latest progress in recent years and the classic small object detection optimization method in the past are described from multi-scale features, novel metric, and super-resolution according to the general principle. Secondly, the detection methods of small objects for specific scenes：aerial images, faces, and pedestrians are summarized. Finally, the possible research directions of small object detection in the future are summarized and proposed. Reference | Related Articles | Metrics

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Improved Yolov7-tiny Algorithm for Steel Surface Defect Detection QI Xiangming, DONG Xu Computer Engineering and Applications    2023, 59 (12): 176-183.   DOI: 10.3778/j.issn.1002-8331.2302-0191 Abstract （401）      PDF（pc） （679KB）（239）       Knowledge map    Save In order to improve the efficiency of small target detection of steel surface defects, an improved Yolov7-tiny steel surface defect detection algorithm is proposed. The activation function of the feature extraction network is changed  to SiLU to improve the feature extraction capability. The tensor splicing operation of the feature fusion network is combined with the weighted bidirectional feature pyramid BiFPN, and the nearest interpolation of the upper sampling part is replaced with the lightweight operator CARAFE to improve the feature fusion ability. Finally, the multi-head self-attention mechanism MHSA and SPD convolution building blocks are introduced at the output end to improve the detection performance of the output end for small targets of steel surface defects. The ablation and contrast experiments are carried out on the NEU-DET dataset. Compared with the original Yolov7-tiny algorithm, the improved algorithm has increased the mAP by 11.7 percentage points, the precision by 3.3 percentage points, and the FPS value reaches 192. The results show that the improved algorithm can effectively improve the detection efficiency of small targets of steel surface defects. Comparative experiments on the VOC2012 dataset show that the improved algorithm is universal. Reference | Related Articles | Metrics

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Graph Convolutional Neural Network and Its Application in Image Recognition LI Wenjing, BAI Jing, PENG Bin, YANG Zhanyuan Computer Engineering and Applications    2023, 59 (22): 15-35.   DOI: 10.3778/j.issn.1002-8331.2302-0273 Abstract （242）      PDF（pc） （803KB）（238）       Knowledge map    Save Convolutional neural network has found widespread application in the field of image recognition, demonstrating remarkable feature extraction capabilities. However, it is inherently designed for processing structured data in Euclidean space, making it less suitable for handling unstructured data. To address this limitation, graph convolutional neural network leverages spectral and spatial methods to extend the scope of convolutional operations, enabling feature learning in non-Euclidean spaces. GCN possesses translational invariance for graph data, facilitating representation learning for unstructured data. Firstly, the basic principles and improvement work of two types of graph convolutional neural networks based on spectral domain and space domain are explained. Then, around the field of image recognition, the application of graph convolutional neural network in multi-label image recognition, skeleton-based action recognition and hyperspectral image classification is introduced, the research progress is summarized, and the performance comparison and analysis of related models are carried out. Finally, the content of the full text is summarized and the future development direction is looked forward. Reference | Related Articles | Metrics

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LSTFormer：Lightweight Semantic Segmentation Network Based on Swin Transformer YANG Cheng, GAO Jianlin, ZHENG Meilin, DING Rong Computer Engineering and Applications    2023, 59 (12): 166-175.   DOI: 10.3778/j.issn.1002-8331.2210-0331 Abstract （389）      PDF（pc） （801KB）（233）       Knowledge map    Save Aiming at the general problem of high computational complexity in existing semantic segmentation networks based on Transformer, a lightweight semantic segmentation network based on Swin Transformer is proposed. Firstly, feature maps of multiple scales are obtained by Swin Transformer. Secondly, the full perception module and the improved cascading fusion module are used to fuse the feature maps of different scales across layers, reducing the semantic gap between the feature maps of different levels. Then, a single Swin Transformer block is introduced to optimize the initial segmentation feature mapping and improve the ability of the network to classify different pixels through the moving window autoattention mechanism. Finally, Dice loss function and cross-entropy loss function are added in the training stage to improve the segmentation performance and convergence speed of the network. The experimental results show that the mIoU of LSTFormer on ADE20K and Cityscapes reaches 49.47% and 81.47%. Compared with similar networks such as SETR and Swin-UPerNet, LSTFormer has lower parameters and computation while maintaining the same segmentation accuracy. Reference | Related Articles | Metrics

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Review of Path Planning Algorithms for Robot Navigation CUI Wei, ZHU Fazheng Computer Engineering and Applications    2023, 59 (19): 10-20.   DOI: 10.3778/j.issn.1002-8331.2301-0088 Abstract （405）      PDF（pc） （595KB）（231）       Knowledge map    Save Path planning is one of the key technologies for robot navigation. An excellent path planning algorithm can quickly find the best collision-free path and improve operational efficiency. Most existing classification methods have difficulty in expressing the differences and connections between algorithms. To distinguish different path planning algorithms more clearly, they are divided into graph-based search, bionic-based, potential field-based, velocity space-based and sampling-based algorithms based on their principle and nature. This paper introduces the concept, characteristics, and development status of each type of algorithm, analyzes the more widely used sample-based algorithms from the perspective of single-query and multi-query algorithms, and the advantages and problems of different types of path planning algorithms are compared and summarized. Finally, the future development trend of robot path planning algorithms in terms of multi-robot collaboration, multi-algorithm fusion and adaptive planning is prospected. Reference | Related Articles | Metrics

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Research on Construction Technology and Development Status of Medical Knowledge Graph HUANG Hexuan, WANG Xiaoyan, GU Zhengwei, LIU Jing, ZANG Yanan, SUN Xin Computer Engineering and Applications    2023, 59 (13): 33-48.   DOI: 10.3778/j.issn.1002-8331.2209-0475 Abstract （260）      PDF（pc） （732KB）（223）       Knowledge map    Save As an important branch of artificial intelligence, knowledge graph can realize comprehensive integration of medical concepts and mining potential medical knowledge due to its powerful semantic processing ability and data organization ability, which has become an important means for the development of medical intelligence. Based on this, the latest methods and features of the four processes of medical knowledge graph building：knowledge extraction, knowledge expression, knowledge fusion and knowledge reasoning are discussed, the advantages and disadvantages of different methods are deeply studied and compared, the commonly used datasets in each stage are summarized, the research status of knowledge graph in medical knowledge question and answer, clinical auxiliary diagnosis and treatment, knowledge mining of traditional Chinese medicine and drug research are  reviewed, the application difficulties in each scenario are analyzed. Finally, the limitations and challenges of the existing medical knowledge graph technology are summarized and its future development is prospected. Reference | Related Articles | Metrics

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Review of Development of Deep Learning Optimizer CHANG Xilong, LIANG Kun, LI Wentao Computer Engineering and Applications    2024, 60 (7): 1-12.   DOI: 10.3778/j.issn.1002-8331.2307-0370 Abstract （171）      PDF（pc） （1327KB）（222）       Knowledge map    Save Optimization algorithms are the most critical  factor in improving the performance of deep learning models, achieved by minimizing the loss function. Large language models (LLMs), such as GPT, have become the research focus in the field of natural language processing, the optimization effect of traditional gradient descent algorithm has been limited. Therefore, adaptive moment estimation algorithms have emerged, which are significantly superior to traditional optimization algorithms in generalization ability. Based on gradient descent, adaptive gradient, and adaptive moment estimation algorithms, and the pros  and cons of optimization algorithms are analyzed. This paper applies optimization algorithms to the Transformer architecture and selects the French-English translation task as the evaluation benchmark. Experiments have shown that adaptive moment estimation algorithms can effectively improve the performance of the model in machine translation tasks. Meanwhile, it discusses the development direction and applications of optimization algorithms. Reference | Related Articles | Metrics

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Review of Application of Machine Learning in Radiomics Analysis LU Huimin, XUE Han, WANG Yilong, WANG Guizeng, SANG Pengcheng Computer Engineering and Applications    2023, 59 (17): 22-34.   DOI: 10.3778/j.issn.1002-8331.2210-0435 Abstract （349）      PDF（pc） （5275KB）（213）       Knowledge map    Save Radiomics is a technique for quantitatively extracting features from standard medical images. The construction of predictive or diagnostic models with the assistance of machine learning enables data to be extracted and applied in clinical decision support systems to improve diagnostic accuracy, which has been widely used in tumor staging, cancer detection, survival analysis and other tasks. The application and research progress of machine learning in radiomics analysis are reviewed. The applicability and limitations of machine learning algorithms in each stage of radiomics analysis are emphatically discussed, and the representative algorithms are thoroughly sorted out and analyzed in terms of principles and application effects. The evaluation methods to the work of each stage in the radiomics analysis are comprehensively introduced. The publicly available medical image datasets and software toolkits for radiomics feature extraction are organized. Finally, the future development of machine learning in radiomics is discussed. Reference | Related Articles | Metrics

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Survey on Video-Text Cross-Modal Retrieval CHEN Lei, XI Yimeng, LIU Libo Computer Engineering and Applications    2024, 60 (4): 1-20.   DOI: 10.3778/j.issn.1002-8331.2306-0382 Abstract （214）      PDF（pc） （3662KB）（211）       Knowledge map    Save Modalities define the specific forms in which data exist. The swift expansion of various modal data types has brought multimodal learning into the limelight. As a crucial subset of this field, cross-modal retrieval has achieved noteworthy advancements, particularly in integrating images and text. However, videos, as opposed to images, encapsulate a richer array of modal data and offer a more extensive spectrum of information. This richness aligns well with the growing user demand for comprehensive and adaptable information retrieval solutions. Consequently, video-text cross-modal retrieval has emerged as a burgeoning area of research in recent times. To thoroughly comprehend video-text cross-modal retrieval and its state-of-the-art developments, a methodical review and summarization of the existing representative methods is conducted. Initially, the focus is on analyzing current deep learning-based unidirectional and bidirectional video-text cross-modal retrieval methods. This analysis includes an in-depth exploration of seminal works within each category, highlighting their strengths and weaknesses. Subsequently, the discussion shifts to an experimental viewpoint, introducing benchmark datasets and evaluation metrics specific to video-text cross-modal retrieval. The performance of several standard methods in benchmark datasets is compared. Finally, the application prospects and future research challenges of video- text cross-modal retrieval are discussed. Reference | Related Articles | Metrics

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Survey on Attack Methods and Defense Mechanisms in Federated Learning ZHANG Shiwen, CHEN Shuang, LIANG Wei, LI Renfa Computer Engineering and Applications    2024, 60 (5): 1-16.   DOI: 10.3778/j.issn.1002-8331.2306-0243 Abstract （180）      PDF（pc） （792KB）（209）       Knowledge map    Save The attack and defense techniques of federated learning are the core issue of federated learning system security. The attack and defense techniques of federated learning can significantly reduce the risk of being attacked and greatly enhance the security of federated learning systems. Deeply understanding the attack and defense techniques of federated learning can advance research in the field and achieve its widespread application of federated learning. Therefore, it is of great significance to study the attack and defense techniques of federated learning. Firstly, this paper briefly introduces the concept, basic workflow, types, and potential existing security issues of federated learning. Subsequently, the paper introduces the attacks that the federated learning system may encounter, and relevant research is summarized during the introduction. Then, starting from whether the federated learning system has targeted defense measures, the defense measures are divided into two categories：universal defense measures and targeted defense measures, and targeted summary are made. Finally, it reviews and analyzes the future research directions for the security of federated learning, providing reference for relevant researchers in their research work on the security of federated learning. Reference | Related Articles | Metrics

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Survey of Few-Shot Image Classification Based on Deep Meta-Learning ZHOU Bojun, CHEN Zhiyu Computer Engineering and Applications    2024, 60 (8): 1-15.   DOI: 10.3778/j.issn.1002-8331.2308-0271 Abstract （123）      PDF（pc） （1091KB）（207）       Knowledge map    Save Deep meta-learning has emerged as a popular paradigm for addressing few-shot classification problems. A comprehensive review of recent advancements in few-shot image classification algorithms based on deep meta-learning is provided. Starting from the problem description, the categorizes of the algorithms based on deep meta-learning for few-shot image classification are summarized, and commonly used few-shot image classification datasets and evaluation criteria are introduced. Subsequently, typical models and the latest research progress are elaborated in three aspects: model-based deep meta-learning methods, optimization-based deep meta-learning methods, and metric-based deep meta-learning methods. Finally, the performance analysis of existing algorithms on popular public datasets is presented, the research hotspots in this topic are summarized, and its future research directions are discussed. Reference | Related Articles | Metrics

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Survey of Research Methods for Low Light Image Enhancement PENG Daxin, ZHEN Tong, LI Zhihui Computer Engineering and Applications    2023, 59 (18): 14-27.   DOI: 10.3778/j.issn.1002-8331.2210-0143 Abstract （374）      PDF（pc） （645KB）（194）       Knowledge map    Save The purpose of low-light image enhancement is to restore images with complete details in low-light conditions, and it has gradually become a hot spot in computer image processing research. The quality of image imaging is crucial to intelligent security, video surveillance, and other scenarios and has a very broad application prospect in related industries. In order to study low-light image enhancement in depth, firstly, the traditional low-light image enhancement methods are classified and analyzed in detail, and then the image enhancement methods based on deep learning are listed, and the various networks used and the problems solved are detailed and compared the mentioned methods in detail. Then, the data set is analyzed and studied in detail, and some commonly used evaluation indicators are briefly sorted out. Finally, it summarizes the content, points out the difficulties in the current research, and points out the research goals for the future. Reference | Related Articles | Metrics

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Survey of Online Course Recommendation System YU Peng, LIU Xingyu, CHENG Hao, YANG Jiaqi, CHEN Guohua, HE Chaobo Computer Engineering and Applications    2023, 59 (22): 1-14.   DOI: 10.3778/j.issn.1002-8331.2305-0162 Abstract （231）      PDF（pc） （692KB）（193）       Knowledge map    Save The rapid development of online education has led to an explosive growth in the number of online courses, and learners are easily caught in inefficient access to course information caused by “course overload”, which has driven the emergence and development of online course recommendation systems. At present, online course recommendation systems have become a hot spot for research, and a large number of methods have been proposed in this area, so it is necessary to systematically review and analyze the latest research progress. This paper first summarizes the basic framework and related concepts of online course recommendation systems, and then focuses on comparing and analyzing various core recommendation methods used in existing online course recommendation systems, including these methods based on association rule mining, matrix factorization, probabilistic model, deep learning, intelligent optimization, semantic computing, and so on. Finally, this paper introduces various evaluation metrics of online course recommendation systems and publicly available datasets, and proposes the future development direction. Reference | Related Articles | Metrics

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Small Object Detection Algorithm Based on ATO-YOLO SU Jia, QIN Yichang, JIA Ze, WANG Jing Computer Engineering and Applications    2024, 60 (6): 68-77.   DOI: 10.3778/j.issn.1002-8331.2308-0385 Abstract （173）      PDF（pc） （795KB）（191）       Knowledge map    Save Small object detection is of great significance in the field of computer vision. However, existing methods often suffer from issues such as missed detection and false alarms when dealing with challenges like scale variation, dense object arrangement, and irregular layouts. To address these problems, ATO-YOLO, an improved version of the YOLOv5 algorithm is proposed. Firstly, this paper introduces an adaptive feature extraction (AFE) module that incorporates an attention mechanism to enhance the feature representation capability of the detection model. By dynamically adjusting the weight allocation to highlight key object features, AFE improves the accuracy and robustness of object detection tasks in various scenarios. Secondly, a triple feature fusion (TFF) mechanism is designed to effectively utilize multi-scale information by fusing feature maps from different scales, resulting in more comprehensive object features and enhanced detection performance for small objects. Lastly, an output reconstruction (ORS) module is introduced, which removes the large object detection layer and adds a small object detection layer, enabling precise localization and recognition of small objects. This module also reduces model complexity and improves detection speed compared to the original model. Experimental results demonstrate that the ATO-YOLO algorithm achieves an mAP@0.5 of 38.2% on the VisDrone dataset, a 6.1?percentage points improvement over YOLOv5, with a relative FPS increase of 4.4%. This algorithm enables fast and accurate detection of small objects. Reference | Related Articles | Metrics

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Improved A* Multi-Robot Bilevel Path Planning Algorithm CHEN Guangyou, YU Su Computer Engineering and Applications    2023, 59 (11): 312-319.   DOI: 10.3778/j.issn.1002-8331.2203-0197 Abstract （241）      PDF（pc） （601KB）（187）       Knowledge map    Save In order to obtain the high-quality collision free path of multi-robots, a multi-robot bilevel programming algorithm based on improved A* algorithm and conflict coordination strategy is proposed. Firstly, in the first layer of the algorithm, the heuristic function of the traditional A* algorithm is improved by introducing the dynamic weight factor combined with planned path information to avoid its blind search, so as to speed up the search speed. Through the screening mechanism and Bezier curve, the actual turning times of the robot are reduced and planned path is smoothed, and then the initial path of a single robot is obtained. Secondly, in the second layer of the algorithm, the time dimension is introduced based on the two-dimensional path to establish the robot path time map, so as to predict the conflict between robots. Finally, the conflict coordination strategy is used to coordinate the conflicts between robots and between robots and dynamic obstacles. The experimental results show that the bilevel programming algorithm can effectively reduce planned path search time and the number of turns, and obtain the smooth and collision free navigation path of each robot. Reference | Related Articles | Metrics

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Survey of Bioinformatics-Based Protein Function Prediction LI Xinhui, QIAN Yurong, YUE Haitao, HU Yue, CHEN Jiaying, LENG Hongyong, MA Mengnan Computer Engineering and Applications    2023, 59 (16): 50-62.   DOI: 10.3778/j.issn.1002-8331.2212-0167 Abstract （322）      PDF（pc） （761KB）（186）       Knowledge map    Save The protein function prediction task aims to provide functional annotations for protein data with missing functional tags. With the development of protein sequencing technology, the number of proteins in the database is growing rapidly, and due to the complexity and multiplicity of protein data, the protein function prediction task is very challenging and has received close attention from researchers. In this paper, the development history of machine learning in protein function prediction is firstly reviewed. Secondly, protein function prediction methods in recent years are categorized and summarized, and the similarities and differences between various algorithms are analyzed. Finally, the problems of protein function prediction are discussed, and future research in this field is anticipated. Reference | Related Articles | Metrics

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Improved Road Damage Detection Algorithm of YOLOv8 LI Song, SHI Tao, JING Fangke Computer Engineering and Applications    2023, 59 (23): 165-174.   DOI: 10.3778/j.issn.1002-8331.2306-0205 Abstract （246）      PDF（pc） （671KB）（183）       Knowledge map    Save Road damage detection is an important task to ensure road safety and realize timely repair of road damage. Aiming at the problems of low detection efficiency, high cost and difficulty in applying to mobile terminal devices in existing Road Damage detection algorithms, a lightweight road damage detection algorithm YOLOV8-Road Damage（YOLOV8-RD） with improved YOLOv8 is proposed. First, combining the advantages of CNN and Transformer, a BOT module that can extract global and local feature information of road damage images is proposed to adapt to the large-span and elongated features of crack objects. Then, coordinate attention（CA） is introduced in the end of backbone network and neck network to embed the location information into the channel attention, strengthen the feature extraction ability, and suppress the interference of irrelevant features. In addition, C2fGhost module is used in YOLOv8 neck network to reduce floating point computation in feature channel fusion process, reduce the number of model parameters, and improve feature expression performance. The experimental results show that in RDD2022 data set and Road Damage data set, the improved algorithm is 2% and 3.7% higher than the original algorithm compared with mAP50, while the number of model parameters is only 2.8×106 and the computation amount is only 7.3×109, which are reduced by 6.7% and 8.5% respectively. The detection speed of the algorithm reaches 88 FPS, which can accurately detect the road damage target in real time. Compared with other mainstream target detection algorithms, the effectiveness and superiority of this method are verified. Reference | Related Articles | Metrics

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Improved YOLOv8 Multi-Scale and Lightweight Vehicle Object Detection Algorithm ZHANG Lifeng, TIAN Ying Computer Engineering and Applications    2024, 60 (3): 129-137.   DOI: 10.3778/j.issn.1002-8331.2309-0145 Abstract （181）      PDF（pc） （713KB）（178）       Knowledge map    Save To address issues such as high hardware requirements, low detection accuracy, and a high rate of missed overlapping targets in traditional vehicle object detection models, a modified vehicle object detection algorithm called RBT-YOLO based on YOLOv8 is proposed. The main network is reconstructed using a multi-scale fusion approach. BiFPN is improved by adding convolutional operations and adjusting input/output channel numbers to adapt to YOLOv8, enhancing its feature fusion capability. After the feature maps are output from the Neck section, a lightweight attention mechanism called Triplet Attention is introduced to enhance the feature extraction ability of the model. To address the issue of high target overlap in real scenarios, SoftNMS (soft non-maximum suppression) is used to replace the original NMS, making the model to handle the candidate boxes more gentle, thereby strengthening detection capabilities of the model and improving recall rates. Experimental results on the Pascal VOC and MS COCO datasets demonstrate that the proposed RBT-YOLO outperforms the original model, reducing parameters and computations by approximately 60%, the mAP improved by 2.6 and 3.0 percentage points, and excelling in both size and precision compared to other classic detection models, thus demonstrating strong practical utility. Reference | Related Articles | Metrics

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Research of Deep Learning-Based Computational Spectral Imaging for Single RGB Image JIANG Zhongmin, ZHANG Wanyan, WANG Wenju Computer Engineering and Applications    2023, 59 (10): 22-34.   DOI: 10.3778/j.issn.1002-8331.2211-0082 Abstract （234）      PDF（pc） （662KB）（177）       Knowledge map    Save Deep learning is introduced into computational spectral imaging to address the high cost and long image acquisition time of traditional spectral imaging methods to investigate how spectral information can be recovered from a single RGB image to provide assistance for various computer vision applications. Computational spectral imaging methods for single RGB images based on deep learning lack comprehensive and systematic research. Deep learning algorithms and network models used for computational spectral imaging are summarised, analyzed and compared. The four categories of CNN（convolutional neural networks）, GAN（generative adversarial networks）, Attention and Transformer are used to sort out supervised learning methods with excellent reconstruction performance in recent years. The unsupervised learning methods are discussed in terms of both self-encoders and domain adaptation. Datasets and evaluation metrics commonly used for the algorithms are listed, and future research trends and development directions are given. Reference | Related Articles | Metrics

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Review of WebAssembly Application Research for Edge Serverless Computing WANG Xin, ZHAO Kai, QIN Bin Computer Engineering and Applications    2023, 59 (11): 28-36.   DOI: 10.3778/j.issn.1002-8331.2210-0308 Abstract （307）      PDF（pc） （575KB）（176）       Knowledge map    Save WebAssembly（Wasm） is a new binary format that is portable, small, fast to load and compatible with the Web. It has the characteristics of high efficiency, security, and openness. The basic concept of edge computing is to run computing tasks on computing resources close to the data source. However, the performance and resources of devices deployed on the edge are usually very limited. In this resource-constrained environment, how to provide low-latency and secure services is an important research direction of edge computing. Serverless is a new way to host applications on infrastructure. At present, it is mainly based on container technology to realize program hosting. Serverless computing is currently the most suitable architecture for edge computing due to its lightweight, function as a service（FaaS）, automatic scaling and so on, but it always has problems such as cold start and large memory consumption. Wasm can replace the traditional container and provide an updated, faster, less resource consuming and secure isolation implementation for edge serverless computing. In this paper, the characteristics of edge serverless computing and its application scenarios, as well as the development trend of Wasm are introduced at first. The current research on Wasm-based edge serverless computing is analyzed and the development direction of Wasm runtime alternative containers as the carrier of edge serverless computing is illustrated. Moreover, the problems of edge Wasm serverless computing platform are discussed and the future optimization directions based on deep reinforcement learning and other artificial intelligence algorithms are summarized. Reference | Related Articles | Metrics

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Experimental Research on Image Recognition of Wire Rope Damage Based on Improved YOLOv5 WANG Hongyao, HAN Shuang, LI Qinyi Computer Engineering and Applications    2023, 59 (17): 99-106.   DOI: 10.3778/j.issn.1002-8331.2210-0505 Abstract （150）      PDF（pc） （3673KB）（172）       Knowledge map    Save Wire rope plays a very important role in coal mine equipment. In order to find the wire rope damage as early as possible, conduct early warning and fault handling in advance, and protect the safety of personnel under the mine, a method of wire rope damage identification and detection based on depth learning is proposed. The target detection algorithm YOLOv5 is adopted and improved. The fast adaptive weighted median filter is used for image pre-processing to improve the recognition accuracy of wire rope damage images. After the improvement, the running speed is increased to 187?ms/piece, and the enhancement effect is good. It integrates CBAM and Transformer prediction heads（TPH） into YOLOv5, and inputs the expanded dataset into the improved model for training and testing. The experimental results show that the improved model has good detection performance, and the final average accuracy rate reaches 0.893, 0.037 higher than the original algorithm, 0.196, 0.162 and 0.102 higher than the traditional detection algorithm SSD, Faster R-CNN and the original YOLOv3, respectively. It shows that the algorithm in this paper has high accuracy and effectively improves the recognition accuracy of wire rope damage images. Reference | Related Articles | Metrics