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Research on Object Detection Algorithm Based on Improved YOLOv5 QIU Tianheng, WANG Ling, WANG Peng, BAI Yan’e Computer Engineering and Applications    2022, 58 (13): 63-73.   DOI: 10.3778/j.issn.1002-8331.2202-0093 Abstract （1622）      PDF（pc） （1109KB）（656）       Knowledge map    Save YOLOv5 is an algorithm with good performance in single-stage target detection at present, but the accuracy of target boundary regression is not too high, so it is difficult to apply to scenarios with high requirements on the intersection ratio of prediction boxes. Based on YOLOv5 algorithm, this paper proposes a new model YOLO-G with low hardware requirements, fast model convergence and high accuracy of target box. Firstly, the feature pyramid network（FPN） is improved, and more features are integrated in the way of cross-level connection, which prevents the loss of shallow semantic information to a certain extent. At the same time, the depth of the pyramid is deepened, corresponding to the increase of detection layer, so that the laying interval of various anchor frames is more reasonable. Secondly, the attention mechanism of parallel mode is integrated into the network structure, which gives the same priority to spatial and channel attention module, then the attention information is extracted by weighted fusion, so that the network can fuse the mixed domain attention according to the attention degree of spatial and channel attention. Finally, in order to prevent the loss of real-time performance due to the increase of model complexity, the network is lightened to reduce the number of parameters and computation of the network. PASCAL VOC datasets of 2007 and 2012 are used to verify the effectiveness of the algorithm. Compared with YOLOv5s, YOLO-G reduces the number of parameters by 4.7% and the amount of computation by 47.9%, while mAP@0.5 and mAP@0.5：0.95 increases by 3.1 and 5.6 percentage points respectively. Reference | Related Articles | Metrics

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Research Progress on Vision System and Manipulator of Fruit Picking Robot GOU Yuanmin, YAN Jianwei, ZHANG Fugui, SUN Chengyu, XU Yong Computer Engineering and Applications    2023, 59 (9): 13-26.   DOI: 10.3778/j.issn.1002-8331.2209-0183 Abstract （1553）      PDF（pc） （787KB）（1179）       Knowledge map    Save Fruit picking robot is of great significance to the realization of automatic intelligence of fruit equipment. In this paper, the research work on the key technologies of fruit-picking robot at home and abroad in recent years is summarized, firstly, the key technologies of fruit-picking robot vision system, such as traditional image segmentation methods based on fruit features, such as threshold method, edge detection method, clustering algorithm based on color features and region-based image segmentation algorithm, are discussed, the object recognition algorithm based on depth learning and the target fruit location are analyzed and compared, and the state-of-the-art of fruit picking robot manipulator and end-effector is summarized, finally, the development trend and direction of fruit-picking robot in the future are prospected, which can provide reference for the related research of fruit-picking robot. Reference | Related Articles | Metrics

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Overview of Multi-Agent Path Finding LIU Zhifei, CAO Lei, LAI Jun, CHEN Xiliang, CHEN Ying Computer Engineering and Applications    2022, 58 (20): 43-64.   DOI: 10.3778/j.issn.1002-8331.2203-0467 Abstract （1550）      PDF（pc） （1013KB）（717）       Knowledge map    Save The multi-agent path finding（MAPF） problem is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other. MAPF is widely used in logistics, military, security and other fields. MAPF algorithm can be divided into the centralized planning algorithm and the distributed execution algorithm when the main research results of MAPF at home and abroad are systematically sorted and classified according to different planning methods. The centralized programming algorithm is not only the most classical but also the most commonly used MAPF algorithm. It is mainly divided into four algorithms based on [A*] search, conflict search, cost growth tree and protocol. The other part of MAPF which is the distributed execution algorithm is based on reinforcement learning. According to different improved techniques, the distributed execution algorithm can be divided into three types：the expert demonstration, the improved communication and the task decomposition. The challenges of existing algorithms are pointed out and the future work is forecasted based on the above classification by comparing the characteristics and applicability of MAPF algorithms and analyzing the advantages and disadvantages of existing algorithms. Reference | Related Articles | Metrics

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Research Progress of Transformer Based on Computer Vision LIU Wenting, LU Xinming Computer Engineering and Applications    2022, 58 (6): 1-16.   DOI: 10.3778/j.issn.1002-8331.2106-0442 Abstract （1471）      PDF（pc） （1089KB）（960）       Knowledge map    Save Transformer is a deep neural network based on the self-attention mechanism and parallel processing data. In recent years, Transformer-based models have emerged as an important area of research for computer vision tasks. Aiming at the current blanks in domestic review articles based on Transformer, this paper covers its application in computer vision. This paper reviews the basic principles of the Transformer model, mainly focuses on the application of seven visual tasks such as image classification, object detection and segmentation, and analyzes Transformer-based models with significant effects. Finally, this paper summarizes the challenges and future development trends of the Transformer model in computer vision. 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 （1464）      PDF（pc） （1009KB）（845）       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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YOLOv5 Helmet Wear Detection Method with Introduction of Attention Mechanism WANG Lingmin, DUAN Jun, XIN Liwei Computer Engineering and Applications    2022, 58 (9): 303-312.   DOI: 10.3778/j.issn.1002-8331.2112-0242 Abstract （1379）      PDF（pc） （1381KB）（772）       Knowledge map    Save For high-risk industries such as steel manufacturing, coal mining and construction industries, wearing helmets during construction is one of effective ways to avoid injuries. For the current helmet wearing detection model in a complex environment for small and dense targets, there are problems such as false detection and missed detection, an improved YOLOv5 target detection method is proposed to detect the helmet wearing. A coordinate attention mechanism（coordinate attention） is added to the backbone network of YOLOv5, which embeds location information into channel attention so that the network can pay attention on a larger area. The original feature pyramid module in the feature fusion module is replaced with a weighted bi-directional feature pyramid（BiFPN）network structure to achieve efficient bi-directional cross-scale connectivity and weighted feature fusion. The experimental results on the homemade helmet dataset show that the improved YOLOv5 model achieves an average accuracy of 95.9%, which is 5.1 percentage points higher than the YOLOv5 model, and meets the requirements for small and dense target detection in complex environments. 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 （1308）      PDF（pc） （605KB）（639）       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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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 （1222）      PDF（pc） （875KB）（733）       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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Survey of Transformer Research in Computer Vision LI Xiang, ZHANG Tao, ZHANG Zhe, WEI Hongyang, QIAN Yurong Computer Engineering and Applications    2023, 59 (1): 1-14.   DOI: 10.3778/j.issn.1002-8331.2204-0207 Abstract （1210）      PDF（pc） （1285KB）（789）       Knowledge map    Save Transformer is a deep neural network based on self-attention mechanism. In recent years, Transformer-based models have become a hot research direction in the field of computer vision, and their structures are constantly being improved and expanded, such as local attention mechanisms, pyramid structures, and so on. Through the improved vision model based on Transformer structure, the performance optimization and structure improvement are reviewed and summarized respectively. In addition，the advantages and disadvantages of the respective structures of the Transformer and convolutional neural network（CNN） are compared and analyzed，and a new hybrid structure of CNN+Transformer is introduced. Finally，the development of Transformer in computer vision is summarized and prospected. Reference | Related Articles | Metrics

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Survey of Opponent Modeling Methods and Applications in Intelligent Game Confrontation WEI Tingting, YUAN Weilin, LUO Junren, ZHANG Wanpeng Computer Engineering and Applications    2022, 58 (9): 19-29.   DOI: 10.3778/j.issn.1002-8331.2202-0297 Abstract （1199）      PDF（pc） （904KB）（473）       Knowledge map    Save Intelligent game confrontation has always been the focus of artificial intelligence research. In the game confrontation environment, the actions, goals, strategies, and other related attributes of agent can be inferred by opponent modeling, which provides key information for game strategy formulation. The application of opponent modeling method in competitive games and combat simulation is promising, and the formulation of game strategy must be premised on the action strategy of all parties in the game, so it is especially important to establish an accurate model of opponent behavior to predict its intention. From three dimensions of connotation, method, and application, the necessity of opponent modeling is expounded and the existing modeling methods are classified. The prediction method based on reinforcement learning, reasoning method based on theory of mind, and optimization method based on Bayesian are summarized. Taking the sequential game（Texas Hold’em）, real-time strategy game（StarCraft）, and meta-game as typical application scenarios, the role of opponent modeling in intelligent game confrontation is analyzed. Finally, the development of adversary modeling technology prospects from three aspects of bounded rationality, deception strategy and interpretability. 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 （1140）      PDF（pc） （662KB）（716）       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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Research on Local Path Planning Algorithm Based on Improved TEB Algorithm DAI Wanyu, ZHANG Lijuan, WU Jiafeng, MA Xianghua Computer Engineering and Applications    2022, 58 (8): 283-288.   DOI: 10.3778/j.issn.1002-8331.2108-0290 Abstract （1114）      PDF（pc） （878KB）（236）       Knowledge map    Save When the traditional TEB（time elastic band） algorithm is used to plan the path in a complex dynamic environment, path vibrations caused by the unsmooth speed control amount will occur, which will bring greater impact to the robot and prone to collisions. Aiming at the above problems, the traditional TEB algorithm is improved. The detected irregular obstacles are expansion treatment and regional classification strategy, and the driving route in the safe area is given priority to make the robot run more safely and smoothly in the complex environment. Adding the obstacle distance to the speed constraint in the algorithm can effectively reduce the vibration amplitude and the impact of the robot during the path driving process caused by the speed jump after the robot approaches the obstacle, so as to ensure the safety of the robot during operation. A large number of comparative simulations in the ROS environment show that in a complex dynamic environment, the path planned by the improved TEB algorithm is safer and smoother, which can effectively reduce the impact of the robot. Reference | Related Articles | Metrics

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Review of Research on Small Target Detection Based on Deep Learning ZHANG Yan, ZHANG Minglu, LYU Xiaoling, GUO Ce, JIANG Zhihong Computer Engineering and Applications    2022, 58 (15): 1-17.   DOI: 10.3778/j.issn.1002-8331.2112-0176 Abstract （1114）      PDF（pc） （995KB）（581）       Knowledge map    Save The task of target detection is to quickly and accurately identify and locate predefined categories of objects from an image. With the development of deep learning techniques, detection algorithms have achieved good results for large and medium targets in the industry. The performance of small target detection algorithms based on deep learning still needs further improvement and optimization due to the characteristics of small targets in images such as small size, incomplete features and large gap between them and the background. Small target detection has a wide demand in many fields such as autonomous driving, medical diagnosis and UAV navigation, so the research has high application value. Based on extensive literature research, this paper firstly defines small target detection and finds the current difficulties in small target detection. It analyzes the current research status from six research directions based on these difficulties and summarizes the advantages and disadvantages of each algorithm. It makes reasonable predictions and outlooks on the future research directions in this field by combining the literature and the development status to provide a certain basic reference for subsequent research. This paper makes a reasonable prediction and outlook on the future research direction in this field, combining the literature and the development status to provide some basic reference for subsequent research. Reference | Related Articles | Metrics

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Review of Deep Reinforcement Learning Model Research on Vehicle Routing Problems YANG Xiaoxiao, KE Lin, CHEN Zhibin Computer Engineering and Applications    2023, 59 (5): 1-13.   DOI: 10.3778/j.issn.1002-8331.2210-0153 Abstract （1096）      PDF（pc） （1036KB）（642）       Knowledge map    Save Vehicle routing problem（VRP） is a classic NP-hard problem, which is widely used in transportation, logistics and other fields. With the scale of problem and dynamic factor increasing, the traditional method of solving the VRP is challenged in computational speed and intelligence. In recent years, with the rapid development of artificial intelligence technology, in particular, the successful application of reinforcement learning in AlphaGo provides a new idea for solving routing problems. In view of this, this paper mainly summarizes the recent literature using deep reinforcement learning to solve VRP and its variants. Firstly, it reviews the relevant principles of DRL to solve VRP and sort out the key steps of DRL-based to solve VRP. Then it systematically classifies and summarizes the pointer network, graph neural network, Transformer and hybrid models four types of solving methods, meanwhile this paper also compares and analyzes the current DRL-based model performance in solving VRP and its variants. Finally, this paper sums up the challenge of DRL-based to solve VRP and future research directions. Reference | Related Articles | Metrics

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Survey of Camera Pose Estimation Methods Based on Deep Learning WANG Jing, JIN Yuchu, GUO Ping, HU Shaoyi Computer Engineering and Applications    2023, 59 (7): 1-14.   DOI: 10.3778/j.issn.1002-8331.2209-0280 Abstract （1091）      PDF（pc） （702KB）（514）       Knowledge map    Save Camera pose estimation is a technology to accurately estimate the 6-DOF position and pose of camera in world coordinate system under known environment. It is a key technology in robotics and automatic driving. With the rapid development of deep learning, using deep learning to optimize camera pose estimation algorithm has become one of the current research hotspots. In order to master the current research status and trends of camera pose estimation algorithms, the mainstream algorithms based on deep learning are summarized. Firstly, the traditional camera pose estimation methods based on feature points is briefly introduced. Then, the camera pose estimation method based on deep learning is mainly introduced. According to the different core algorithms, the end-to-end camera pose estimation, scene coordinate regression, camera pose estimation based on retrieval, hierarchical structure, multi-information fusion and cross scenescamera pose estimation are elaborated and analyzed in detail. Finally, this paper summarizes the current research status, points out the challenges in the field of camera pose estimation based on in-depth performance analysis, and prospects the development trend of camera pose estimation. Reference | Related Articles | Metrics

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Overview of Image Edge Detection XIAO Yang, ZHOU Jun Computer Engineering and Applications    2023, 59 (5): 40-54.   DOI: 10.3778/j.issn.1002-8331.2209-0122 Abstract （1066）      PDF（pc） （921KB）（574）       Knowledge map    Save The task of edge detection is to identify pixels with significant brightness changes as target edges, which is a low-level problem in computer vision, and edge detection has important applications in object recognition and detection, object proposal generation, and image segmentation. Nowadays, edge detection has produced several types of methods, such as traditional gradient-based detection methods and deep learning-based edge detection algorithms and detection methods combined with emerging technologies. A finer classification of these methods provides researchers with a clearer understanding of the trends in edge detection. Firstly, the theoretical basis and implementation methods of traditional edge detection are introduced; then the main edge detection methods in recent years are summarized and classified according to the methods used, and the core techniques used in them are introduced, such as branching structure, feature fusion and loss function. The evaluation indicators used to assess the algorithm’s performance are single-image optimal threshold（ODS） and frame per second（FPS）, which are contrasted using the fundamental data set（BSDS500）. Finally, the current state of edge detection research is examined and summarized, and the possible future research directions of edge detection are prospected. Reference | Related Articles | Metrics

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Overview of Smoke and Fire Detection Algorithms Based on Deep Learning ZHU Yuhua, SI Yiyi, LI Zhihui Computer Engineering and Applications    2022, 58 (23): 1-11.   DOI: 10.3778/j.issn.1002-8331.2206-0154 Abstract （1051）      PDF（pc） （782KB）（556）       Knowledge map    Save Among various disasters, fire is one of the main disasters that most often and universally threaten public safety and social development. With the rapid development of economic construction and the increasing size of cities, the number of major fire hazards has increased dramatically. However, the widely used smoke sensor method of fire detection is vulnerable to factors such as distance, resulting in untimely detection. The introduction of video surveillance systems has provided new ideas to solve this problem. Traditional image processing algorithms based on video are earlier proposed methods, and the recent rapid development of machine vision and image processing technologies has resulted in a series of methods using deep learning techniques to automatically detect fires in video and images, which have very important practical applications in the field of fire safety. In order to comprehensively analyze the improvements and applications related to deep learning methods for fire detection, this paper first briefly introduces the fire detection process based on deep learning, and then focuses on a detailed comparative analysis of deep methods for fire detection in three granularities：classification, detection, and segmentation, and elaborates the relevant improvements taken by each class of algorithms for existing problems. Finally, the problems of fire detection at the present stage are summarized and future research directions are proposed. 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 （1023）      PDF（pc） （854KB）（689）       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 of Deep Clustering Algorithm Based on Autoencoder TAO Wenbin, QIAN Yurong, ZHANG Yiyang, MA Hengzhi, LENG Hongyong, MA Mengnan Computer Engineering and Applications    2022, 58 (18): 16-25.   DOI: 10.3778/j.issn.1002-8331.2204-0049 Abstract （1013）      PDF（pc） （724KB）（367）       Knowledge map    Save As a common analysis method, cluster analysis is widely used in various scenarios. With the development of machine learning technology, deep clustering algorithm has also become a hot research topic, and the deep clustering algorithm based on autoencoder is one of the representative algorithms. To keep abreast of the development of deep clustering algorithms based on autoencoders, four models of autoencoders are introduced, and the representative algorithms in recent years are classified according to the structure of autoencoders. For the traditional clustering algorithm and the deep clustering algorithm based on autoencoder, experiments are compared and analyzed on the MNIST, USPS, Fashion-MNIST datasets. At last, the current problems of deep clustering algorithms based on autoencoders are summarized, and the possible research directions of deep clustering algorithms are prospected. Reference | Related Articles | Metrics

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Review of Research on Driver Fatigue Driving Detection Methods ZHANG Rui, ZHU Tianjun, ZOU Zhiliang, SONG Rui Computer Engineering and Applications    2022, 58 (21): 53-66.   DOI: 10.3778/j.issn.1002-8331.2204-0053 Abstract （951）      PDF（pc） （946KB）（435）       Knowledge map    Save The proportion of traffic accidents caused by fatigue driving has increased year by year, which has attracted widespread attention from researchers. At present, the research of fatigue driving testing is limited by various factors such as scientific and technological level, environment, and road, which makes it difficult to further develop fatigue driving detection technology. This article introduces the latest progress in driver fatigue driving detection methods in the past decade. The two categories of active detection method and passive detection method are elaborated and reviewed. According to the different characteristics of the two major types of detection methods, it is carefully classified. The advantages and limitations of various fatigue driving detection methods are further analyzed, and the detection algorithms used in the active detection method based on facial features in the past three years are analyzed and summarized. Finally, the shortcomings of various fatigue driving detection methods are summarized, and the future research trends in the field of fatigue detection are proposed, which provides new ideas for researchers to further research. 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 （941）      PDF（pc） （683KB）（587）       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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Systematic Review on Graph Deep Learning in Medical Image Segmentation WANG Guoli, SUN Yu, WEI Benzheng Computer Engineering and Applications    2022, 58 (12): 37-50.   DOI: 10.3778/j.issn.1002-8331.2112-0225 Abstract （935）      PDF（pc） （1194KB）（391）       Knowledge map    Save High precision segmentation of organs or lesions in medical image is a vital challenge issue for intelligent analysis of medical image, it has important clinical application value for auxiliary diagnosis and treatment of diseases. Recently, in solving challenging problems such as medical image information representation and accurate modeling of non-Euclidean spatial physiological tissue structures, the graph deep learning based medical image segmentation technology has made important breakthroughs, and it has shown significant information feature extraction and characterization advantages. The merged technology also can obtain more accurate segmentation results, which has become an emerging research hotspot in this field. In order to better promote the research and development of the deep learning segmentation algorithm for medical image graphs, this paper makes a systematic summary of the technological progress and application status in this field. The paper introduces the definition of graphs and the basic structure of graph convolutional networks, and elaborates on spectral graph convolution and spatial graph convolution operations. Then, according to the three technical structure modes of GCN combined with residual module, attention mechanism module and learning module, the research progress in medical image segmentation has been encapsulated. The application and development of graph deep learning algorithms based medical image segmentation are summarized and prospected to provide references and guiding principles for the technical development of related researches. Reference | Related Articles | Metrics

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Review of Visual Odometry Methods Based on Deep Learning ZHI Henghui, YIN Chenyang, LI Huibin Computer Engineering and Applications    2022, 58 (20): 1-15.   DOI: 10.3778/j.issn.1002-8331.2203-0480 Abstract （920）      PDF（pc） （904KB）（501）       Knowledge map    Save Visual odometry（VO） is a common method to deal with the positioning of mobile devices equipped with vision sensors, and has been widely used in autonomous driving, mobile robots, AR/VR and other fields. Compared with traditional model-based methods, deep learning-based methods can learn efficient and robust feature representations from data without explicit computation, thereby improving their ability to handle challenging scenes such as illumination changes and less textures. In this paper, it first briefly reviews the model-based visual odometry methods, and then focuses on six aspects of deep learning-based visual odometry methods, including supervised learning methods, unsupervised learning methods, model-learning fusion methods, common datasets, evaluation metrics, and comparison of models and deep learning methods. Finally, existing problems and future development trends of deep learning-based visual odometry are discussed. Reference | Related Articles | Metrics

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Survey on Image Semantic Segmentation in Dilemma of Few-Shot WEI Ting, LI Xinlei, LIU Hui Computer Engineering and Applications    2023, 59 (2): 1-11.   DOI: 10.3778/j.issn.1002-8331.2205-0496 Abstract （918）      PDF（pc） （4301KB）（697）       Knowledge map    Save In recent years, image semantic segmentation has developed rapidly due to the emergence of large-scale datasets. However, in practical applications, it is not easy to obtain large-scale, high-quality images, and image annotation also consumes a lot of manpower and time costs. In order to get rid of the dependence on the number of samples, few-shot semantic segmentation has gradually become a research hotspot. The current few-shot semantic segmentation methods mainly use the idea of meta-learning, which can be divided into three categories：based on the siamese neural network, based on the prototype network and based on the attention mechanism according to different model structures. Based on the current research, this paper introduces the development, advantages and disadvantages of various methods for few-shot semantic segmentation, as well as common datasets and experimental designs. On this basis, the application scenarios and future development directions are summarized. Reference | Related Articles | Metrics

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Study on Optimization of Cooperative Distribution Path Between UAVs and Vehicles Under Rural E-Commerce Logistics XU Ling, YANG Linchao, ZHU Wenxing, ZHONG Shaojun Computer Engineering and Applications    2024, 60 (1): 310-318.   DOI: 10.3778/j.issn.1002-8331.2306-0115 Abstract （899）      PDF（pc） （666KB）（748）       Knowledge map    Save Drone delivery has emerged as a significant solution to address the challenges of last-mile logistics. The collaborative delivery model between drones and vehicles overcomes the limitations of insufficient drone delivery capacity and enhances safety, making it a vital approach for drone involvement in the delivery process. To tackle the difficulties and high costs associated with “last-mile” delivery in rural e-commerce logistics, this study constructs a mixed-integer programming model. The objective is to minimize delivery costs while considering constraints such as the collaborative drone-vehicle mode and multi drone multi-parcel delivery. A two-stage algorithm is proposed to optimize the paths for drone-vehicle collaborative delivery. In the first stage, a constrained adaptive K-means algorithm is utilized to determine the range of vehicle docking points. In the second stage, an improved genetic algorithm that incorporates hill climbing and splitting operators is employed to identify the optimal delivery paths for drones and vehicles. Subsequently, a case study experiment is conducted to validate the feasibility and effectiveness of the model and algorithm. The research findings are expected to offer novel insights and valuable references for cost reduction and efficiency improvement in last-mile delivery for rural e-commerce logistics. Reference | Related Articles | Metrics

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Survey of Research on Deep Multimodal Representation Learning PAN Mengzhu, LI Qianmu, QIU Tian Computer Engineering and Applications    2023, 59 (2): 48-64.   DOI: 10.3778/j.issn.1002-8331.2206-0145 Abstract （891）      PDF（pc） （6521KB）（636）       Knowledge map    Save Although deep learning has been widely used in many fields because of its powerful nonlinear representation capabilities, the structural and semantic gap between multi-source heterogeneous modal data seriously hinders the application of subsequent deep learning models. Many scholars have proposed a large number of representation learning methods to explore the correlation and complementarity between different modalities, and improve the performance of deep learning prediction and generalization. However, the research on multimodal representation learning is still in its infancy, and there are still many scientific problems to be solved. So far, multimodal representation learning still lacks a unified cognition, and the architecture and evaluation metrics of multimodal representation learning research are not fully clear. According to the feature structure, semantic information and representation ability of different modalities, this paper studies and analyzes the progress of deep multimodal representation learning from the perspectives of representation fusion and representation alignment. And the existing research work is systematically summarized and scientifically classified. At the same time, this paper analyzes the basic structure, application scenarios and key issues of representative frameworks and models, analyzes the theoretical basis and latest development of deep multimodal representation learning, and points out the current challenges and future development of multimodal representation learning research, to further promote the development and application of deep multimodal representation learning. Reference | Related Articles | Metrics

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Small Object Detection Algorithm Based on Improved YOLOv5 in UAV Image XIE Chunhui, WU Jinming, XU Huaiyu Computer Engineering and Applications    2023, 59 (9): 198-206.   DOI: 10.3778/j.issn.1002-8331.2212-0336 Abstract （836）      PDF（pc） （808KB）（519）       Knowledge map    Save UAV aerial images have many characteristics, such as large-scale changes and complex backgrounds, so it is difficult for the existing detectors to detect small objects in aerial images. Aiming at the problem of mistake detection and omission, a small object detection algorithm model Drone-YOLO is proposed. A new detection branch is added to improve the detection capability at multiple scales, meanwhile the model contains a novel feature pyramid network with multi-level information aggregation, which realizes the fusion of cross-layers information. Then a feature fusion module based on multi-scale channel attention mechanism is designed to improve the focus on small objects. The classification task of the prediction head is decoupled from the regression task, and the loss function is optimized using Alpha-IoU to improve the accuracy of detection. The experimental results of VisDrone dataset show that the Drone-YOLO has improved the AP50 by 4.91?percentage points compared with the YOLOv5, and the inference time is only 16.78?ms. Compared with other mainstream models, it has a better detection effect for small targets, and can effectively complete the task of small target detection in UAV aerial images. 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 （817）      PDF（pc） （830KB）（319）       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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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 （799）      PDF（pc） （977KB）（521）       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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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 （794）      PDF（pc） （929KB）（500）       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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Summary of Intrusion Detection Models Based on Deep Learning ZHANG Hao, ZHANG Xiaoyu, ZHANG Zhenyou, LI Wei Computer Engineering and Applications    2022, 58 (6): 17-28.   DOI: 10.3778/j.issn.1002-8331.2107-0084 Abstract （768）      PDF（pc） （997KB）（482）       Knowledge map    Save With the continuous in-depth development of deep learning technology, intrusion detection model based on deep learning has become a research hotspot in the field of network security. This paper summarizes the commonly used data preprocessing operations in network intrusion detection. The popular intrusion detection models based on deep learning, such as convolutional neural network, long short-term memory network, auto-encode and generative adversarial networks, are analyzed and compared. The data sets commonly used in the research of intrusion detection model based on deep learning are introduced. It points out the problems of the existing intrusion detection models based on deep learning in data set timeliness, real-time, universality, model training time and other aspects, and the possible research focus in the future. Reference | Related Articles | Metrics

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Research Progress in Application of Graph Anomaly Detection in Financial Anti-Fraud LIU Hualing, LIU Yaxin, XU Junyi, CHEN Shanghui, QIAO Liang Computer Engineering and Applications    2022, 58 (22): 41-53.   DOI: 10.3778/j.issn.1002-8331.2203-0233 Abstract （740）      PDF（pc） （1848KB）（515）       Knowledge map    Save With the rapid development of digital finance, fraud presents new characteristics such as intellectualization, industrialization and strong concealment. And the limitations of traditional expert rules and machine learning methods are increa- singly apparent. Graph anomaly detection technology has a strong ability to deal with associated information, which provides new idea for financial anti-fraud. Firstly, the development and advantages of graph anomaly detection are briefly introduced. Secondly, from the perspectives of individual anti-fraud and group anti-fraud, graph anomaly detection technology is divided into individual fraud detections based on feature, proximity, graph representation learning or community division, and gang fraud detections based on dense subgraph, dense subtensor or deep network structure. The basic idea, advantages, disadvantages, research progress and typical applications of each anomaly detection technology are analyzed and compared. Finally, the common test data sets and evaluation criteria are summarized, and the development prospect and research direction of graph anomaly detection technology in financial anti-fraud are given. Reference | Related Articles | Metrics

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Research on Transformer-Based Single-Channel Speech Enhancement FAN Junyi, YANG Jibin, ZHANG Xiongwei, ZHENG Changyan Computer Engineering and Applications    2022, 58 (12): 25-36.   DOI: 10.3778/j.issn.1002-8331.2201-0371 Abstract （730）      PDF（pc） （1155KB）（308）       Knowledge map    Save Deep learning can effectively solve the complex mapping problem between noisy speech signals and clean speech signals to improve the quality of single-channel speech enhancement, but the enhancement effect based on network models is not satisfactory. Transformer has been widely used in the field of speech signal processing due to the fact that it integrates multi-headed attention mechanism and can focus on the long-term correlation existing in speech. Based on this, deep learning-based speech enhancement models are reviewed,  the Transformer model and its internal structure are summarized, Transformer-based speech enhancement models are classified in terms of different implementation structures, and several example models are analyzed in detail. Furthermore, the performance of Transformer-based single-channel speech enhancement is compared on the public datasets, and their advantages and disadvantages are analyzed. The shortcomings of the related research work are summarized and future developments are envisaged. 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 （719）      PDF（pc） （954KB）（528）       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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Research on Urban Logistics Distribution Mode of Bus-Assisted Drones PENG Yong, REN Zhi Computer Engineering and Applications    2024, 60 (7): 335-343.   DOI: 10.3778/j.issn.1002-8331.2212-0252 Abstract （719）      PDF（pc） （755KB）（592）       Knowledge map    Save The rapid development of e-commerce forces the continuous transformation and upgrading of the logistics industry. In view of the fact that local governments encourage the development of public transport and advocate green and low-carbon logistics distribution mode, a distribution mode of bus-assisted drone is studied. After explaining the problem, a mathematical model with the lowest distribution cost is constructed, and a heuristic algorithm of smart general variable neighborhood search metaheuristic is designed to solve the problem. At the same time, in order to improve the efficiency of the algorithm, K-means clustering and greedy algorithm are introduced to generate the initial solution. Firstly, aiming at different scale examples, a variety of local search strategies and a variety of algorithms are compared to verify the effectiveness of the algorithm. Secondly, by selecting the standard CVRP as example, the single truck distribution mode and truck-drone collaborative distribution mode are compared with the distribution mode of bus-assisted drone to prove its cost and time advantages. Finally, Beijing Bus Rapid Transit Line 2 and its surrounding customer points are selected, and sensitivity analysis is made by changing the bus stop spacing and departure interval, result shows that the impact of increasing the stop spacing is greater than the change of departure interval. Reference | Related Articles | Metrics

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Study on Hierarchical Multi-Label Text Classification Method of MSML-BERT Model HUANG Wei, LIU Guiquan Computer Engineering and Applications    2022, 58 (15): 191-201.   DOI: 10.3778/j.issn.1002-8331.2111-0176 Abstract （707）      PDF（pc） （1333KB）（304）       Knowledge map    Save Hierarchical multi-label text classification is more challenging than ordinary multi-label text classification, since multiple labels of the text establish a tree-like hierarchy. Current methods use the same model structure to predict labels at different layers, ignoring their differences and diversity. They don’t model the hierarchical dependencies fully, resulting in poor prediction performance of labels at all layers, especially the lower-layer long-tail labels, and may lead to label inconsistency problems. In order to address the above problems, the multi-task learning architecture is introduced, and the MSML-BERT model is proposed. The model regards the label classification network of each layer in the label hierarchy as a learning task, and enhances the performance of tasks at all layers through the sharing and transfer of knowledge between tasks. Based on this, a multi-scale feature extraction module is designed to capture multi-scale and multi-grained features to form various knowledge required at different layers. Further, a multi-layer information propagation module is designed to fully model hierarchical dependencies and transfer knowledge in different layers to support lower-layer tasks. In this module, a hierarchical gating mechanism is designed to filter the knowledge flow among tasks in different layers. Extensive experiments are conducted on the RCV1-V2, NYT and WOS datasets, and the results reveal that the entire performance of this model, especially on the lower-layer long-tail labels, surpasses that of other prevailing models and maintains a low label inconsistency ratio. Reference | Related Articles | Metrics

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Overview of Human Behavior Recognition Based on Deep Learning DENG Miaolei, GAO Zhendong, LI Lei, CHEN Si Computer Engineering and Applications    2022, 58 (13): 14-26.   DOI: 10.3778/j.issn.1002-8331.2201-0096 Abstract （695）      PDF（pc） （676KB）（346）       Knowledge map    Save Human behavior recognition aims to retrieve and identify the target behavior in surveillance video, which is a research hotspot in the field of artificial intelligence. Human behavior recognition algorithm based on traditional methods has some shortcomings, such as large dependence on sample data and easy to be affected by environmental noise. In order to solve this problem, many human behavior recognition algorithms based on deep learning are proposed for different application scenarios. Firstly, the traditional feature extraction methods and feature extraction methods based on deep learning in human behavior recognition task are introduced. Secondly, the human behavior recognition algorithms based on deep learning are summarized from two aspects of performance and application. The idea of human behavior recognition method based on 3D convolutional neural network, hybrid network, two-stream convolutional neural network and few-shot learning（FSL） and its performance on UCF101 and HMDB51 datasets are analyzed. Thirdly, on the basis of deep learning, the advantages, disadvantages and effectiveness of mainstream model migration methods are summarized. Finally, the shortcomings of existing human behavior recognition algorithms based on deep learning are summarized, and the possibility of FSL algorithm represented by meta-learning and transformer that will become the mainstream algorithm of future models is discussed. At the same time, the future development direction of human behavior recognition based on deep learning is prospected. Reference | Related Articles | Metrics

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Survey on Attention Mechanisms in Deep Learning Recommendation Models GAO Guangshang Computer Engineering and Applications    2022, 58 (9): 9-18.   DOI: 10.3778/j.issn.1002-8331.2112-0382 Abstract （661）      PDF（pc） （944KB）（345）       Knowledge map    Save Aims to explore how the attention mechanism helps the recommendation model to dynamically focus on specific parts of the input that help to perform the current recommendation task. This paper analyzes the attention mechanism network framework and the weight calculation method of its input data, and then summarizes from the five perspectives of vanillaattention mechanism, co-attention mechanism, self-attention mechanism, hierarchical attention mechanism, and multi-head attention mechanism. Analyze how it uses key strategies, algorithms, or techniques to calculate the weight of the current input data, and use the calculated weights so that the recommendation model can focus on the necessary parts of the input at each step of the recommendation task, more effective user or item feature representation can be generated, and the operating efficiency and generalization ability of the recommendation model are improved. The attention mechanism can help the recommendation model assign different weights to each part of the input, extract more critical and important information, and enable the recommendation model to make more accurate judgments, and it will not bring more overhead to the calculation and storage of the recommendation model. Although the existing deep learning recommendation model with the attention mechanism can meet the needs of most recommendation tasks to a certain extent, it is certain that the uncertainty of human needs and the explosive growth of information under certain circumstances factors, it will still face the challenges of recommendation diversity, recommendation interpretability, and the integration of multiple auxiliary information. Reference | Related Articles | Metrics

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Overview of Cross-Modal Retrieval Technology XU Wenwan, ZHOU Xiaoping, WANG Jia Computer Engineering and Applications    2022, 58 (23): 12-23.   DOI: 10.3778/j.issn.1002-8331.2205-0160 Abstract （658）      PDF（pc） （769KB）（249）       Knowledge map    Save Cross modal retrieval can retrieve the information of other models through one model, which has become a research hot-spot in the era of big data. Researchers based on real value representation and binary representation to reduce the semantic gap of different modal information and compare the similarity effectively, but there will still be the problem of low retrieval efficiency or information loss. At present, how to further improve retrieval efficiency and information utilization is a key challenge for cross modal retrieval research. Firstly, the development status of real value representation and binary representation in cross-modal retrieval is introduced. Secondly, it analyzes and compares five cross modal retrieval methods based on modeling technology and similarity comparison under two presentation technologies：subspace learning, topic statistical model learning, deep learning, traditional hash and deep hash. Then, the latest multi-modal datasets are summarized to provide valuable reference for relevant researchers and engineers. Finally, the challenges of cross modal retrieval are analyzed and the future research directions in this field are pointed out. 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 （640）      PDF（pc） （674KB）（398）       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