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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
    Abstract671)      PDF(pc) (1381KB)(498)       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.
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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
    Abstract629)      PDF(pc) (1089KB)(533)       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.
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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
    Abstract529)      PDF(pc) (904KB)(164)       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.
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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
    Abstract376)      PDF(pc) (904KB)(256)       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.
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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
    Abstract336)      PDF(pc) (878KB)(89)       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.
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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
    Abstract290)      PDF(pc) (1848KB)(240)       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.
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    TLS Malicious Encrypted Traffic Identification Research
    KANG Peng, YANG Wenzhong, MA Hongqiao
    Computer Engineering and Applications    2022, 58 (12): 1-11.   DOI: 10.3778/j.issn.1002-8331.2110-0029
    Abstract285)      PDF(pc) (747KB)(183)       Save
    With the advent of the 5G era and the increasing public awareness of the Internet, the public has paid more and more attention to the protection of personal privacy. Due to malicious communication in the process of data encryption, to ensure data security and safeguard social and national interests, the research work on encrypted traffic identification is particularly important. Therefore, this paper describes the TLS traffic in detail and analyzes the improved technology of early identification method, including common traffic detection technology, DPI detection technology, proxy technology, and certificate detection technology. It also introduces machine learning models for selecting different TLS encrypted traffic characteristics, as well as many recent research results of deep learning models without feature selection. The deficiencies of the related research work are summarized, and the future research work and development trend of the technology have been prospected.
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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
    Abstract284)      PDF(pc) (724KB)(138)       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.
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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
    Abstract271)      PDF(pc) (1109KB)(209)       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.
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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
    Abstract264)      PDF(pc) (1155KB)(134)       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.
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    Review of Cognitive and Joint Anti-Interference Communication in Unmanned System
    WANG Guisheng, DONG Shufu, HUANG Guoce
    Computer Engineering and Applications    2022, 58 (8): 1-11.   DOI: 10.3778/j.issn.1002-8331.2109-0334
    Abstract260)      PDF(pc) (913KB)(244)       Save
    As the electromagnetic environment becomes more and more complex as well as the confrontation becomes more and more intense, it puts forward higher requirements for the reliability of information transmission of unmanned systems whereas the traditional cognitive communication mode is difficult to adapt to the independent and distributed development trend of broadband joint anti-interference in future. For the need of low anti-interference intercepted communications surrounded in unmanned systems, this paper analyzes the cognitive anti-interference technologies about interference detection and identification, transformation analysis and suppression in multiple domains and so on. The research status of common detection and estimation, classification and recognition are summarized. Then, typical interference types are modeled correspondingly, and transformation methods and processing problems are concluded. Furthermore, traditional interference suppression methods and new interference suppression methods are systematically summarized. Finally, the key problems of restricting the joint interference of broadband are addressed, such as the classification and recognition of unknown interference, the temporal elimination of multiple interference, the joint separation of distributed interference and the optimal control of collaborative interference, which highlight the important role of cognitive interference suppression technology in unmanned system communication.
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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
    Abstract244)      PDF(pc) (997KB)(215)       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.
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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
    Abstract220)      PDF(pc) (1013KB)(88)       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.
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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
    Abstract219)      PDF(pc) (944KB)(138)       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.
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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
    Abstract212)      PDF(pc) (995KB)(170)       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.
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    Real Time Traffic Sign Detection Algorithm Based on Improved YOLOV3
    WANG Hao, LEI Yinjie, CHEN Haonan
    Computer Engineering and Applications    2022, 58 (8): 243-248.   DOI: 10.3778/j.issn.1002-8331.2011-0460
    Abstract210)      PDF(pc) (625KB)(178)       Save
    Traffic sign detection is an important part of intelligent driving task. In order to meet the requirements of detection accuracy and real-time detection, an improved real-time traffic sign detection algorithm based on YOLOV3 is proposed. First, the cross stage local network is used as the feature extraction module to optimize the gradient information and reduce the inference computation. At the same time, the path aggregation network is used to replace the feature pyramid network, which not only solves the multi-scale feature fusion, but also preserves more accurate target spatial information and improves the targets detection accuracy. In addition, the complete intersection over union loss function is introduced to replace the mean square error loss to improve the positioning accuracy. Compared with other object detection algorithm on the CCTSDB dataset, experimental results show that, the average precision of the improved algorithm reaches 95.2% and the detection speed reaches 113.6 frame per second, which is 2.37% and 142% higher than YOLOV3 algorithm.
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    CA-YOLOv5 for Crowded Pedestrian Detection
    CHEN Yixiao, Alifu·Kuerban, LIN Wenlong, YUAN Xu
    Computer Engineering and Applications    2022, 58 (9): 238-245.   DOI: 10.3778/j.issn.1002-8331.2201-0058
    Abstract200)      PDF(pc) (1115KB)(155)       Save
    Aiming at the problem of high miss-detection rate and insufficient feature fusion of YOLOv5 in crowded pedestrian detection task, the CA-YOLOv5 pedestrian detection algorithm is proposed. To solve the problem of insufficient fine-grained feature fusion in the original backbone network, Res2Block is used to rebuild the backbone network of YOLOv5, so as to improve the fine-grained feature fusion ability of the network and improve the detection accuracy. For the large change of target scale in dataset, coordinate attention is introduced to enhance the receptive field and the model’s ability to accurately locate the target. Aiming at the problem that FPN structure reduces the multi-scale feature expression ability during feature fusion, the feature enhancement module is proposed to enhance the multi-scale feature expression ability. Through the structural re-parameterization method to reduce the number of parameters and computation in the model, and speed up the detection. Aiming at the common problem of crowded pedestrians in pedestrian detection task, EViT is proposed to enhance the ability of the model to pay attention to local information and improve the detection accuracy. Experimental results show that in the crowded pedestrian detection task, the detection accuracy of CA-YOLOv5 reaches 84.86%, 3.75% higher than the original algorithm, and the detection speed can reach 51?FPS, which has good detection accuracy and real-time. Therefore, it can be better applied to real-time pedestrian detection task.
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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
    Abstract199)      PDF(pc) (782KB)(196)       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.
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    Lightweight Helmet Wearing Detection Algorithm of Improved YOLOv5
    YANG Yongbo, LI Dong
    Computer Engineering and Applications    2022, 58 (9): 201-207.   DOI: 10.3778/j.issn.1002-8331.2111-0346
    Abstract197)      PDF(pc) (1476KB)(164)       Save
    Aiming at the problems of the existing helmet wearing detection algorithm, such as multiple parameters, complex network, large amount of calculation, which is not suitable for deployment on embedded devices, and poor discrimination of occlusion targets, an improved lightweight helmet detection algorithm, YOLo-M3, is proposed.?Firstly, the YOLOv5s backbone network is replaced by MobileNetV3 for feature extraction, which reduces the number of parameters and computation of the network.?Secondly, Diou-NMS is used to replace NMS to improve the identification of occlusion targets. CBAM attention mechanism is added to make the model pay more attention to the main information to improve the detection accuracy. Finally, knowledge distillation is carried out to increase the recall rate and accuracy of model detection.?Experiments verify that YOLO-M3 algorithm can improve the identification of occlusion targets, and reduce the calculation amount of YOLOv5s model by 42% and the model size by 40% while ensuring a high average detection accuracy, thus reducing the hardware cost and meeting the requirements of deployment in embedded end.
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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
    Abstract196)      PDF(pc) (1194KB)(123)       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.
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    Review of Research on Face Mask Wearing Detection
    WANG Xinran, TIAN Qichuan, ZHANG Dong
    Computer Engineering and Applications    2022, 58 (10): 13-26.   DOI: 10.3778/j.issn.1002-8331.2110-0396
    Abstract187)      PDF(pc) (733KB)(164)       Save
    Face mask wearing detection is an emerging research topic that has developed rapidly in the past two years in the context of the global COVID-19 epidemic. Under regular epidemic situation, wearing masks is an important means of effective epidemic prevention, therefore it is essential to remind and check people whether to wear masks in public places. Using artificial intelligence to complete mask wearing detection can achieve the purpose of real-time supervision, save human resources and effectively avoid mistakes, missed detection and other problems. The models and relevant algorithms used in current mask wearing detection research are reviewed. Firstly, the task and application background of mask wearing detection are described. Then, the detection algorithms based on deep neural networks and object detection models are summarized and  analyzed, the advantages and disadvantages, improvement methods and application scenarios of different research schemes are discussed. Secondly, common related data sets are introduced, and the detection performance of each algorithm is compared. Finally, the existing problems and the direction of future development are discussed and prospected.
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    Research and Prospect of Brain-Inspired Model for Visual Object Recognition
    YANG Xi, YAN Jie, WANG Wen, LI Shaoyi, LIN Jian
    Computer Engineering and Applications    2022, 58 (7): 1-20.   DOI: 10.3778/j.issn.1002-8331.2110-0253
    Abstract187)      PDF(pc) (906KB)(135)       Save
    Visual object recognition is one of the most fundamental and challenging research topics in the field of computer vision. The research on the neural mechanism of the primates’ recognition function may bring revolutionary breakthroughs in brain-inspired vision. This review aims to systematically review the recent works on the intersection of computational neuroscience and computer vision. It attempts to investigate the current brain-inspired object recognition models and their underlying visual neural mechanism. According to the technical architecture and exploitation methods, the paper describes the brain-inspired object recognition models and their advantages and disadvantages in realizing brain-inspired object recognition. It focuses on analyzing the similarity between the artificial and biological neural network, and studying the biological credibility of the current popular DNN-based visual benchmark models. The analysis provides a guide for researchers to measure the occasion and condition when conducting visual object recognition research.
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    Survey of Attribute Graph Anomaly Detection Based on Deep Learning
    ZHANG Yiyang, QIAN Yurong, TAO Wenbin, LENG Hongyong, LI Zichen, MA Mengnan
    Computer Engineering and Applications    2022, 58 (19): 1-13.   DOI: 10.3778/j.issn.1002-8331.2204-0315
    Abstract183)      PDF(pc) (869KB)(151)       Save
    Anomaly detection has always been one of the research hotspots in the field of data mining, and its task is to identify rare observations in massive data. With the development of graph data mining, attribute graph anomaly detection has received wide attention in various areas. However, attribute graphs have become a difficult problem in anomaly detection due to their complex topology and rich attribute information. Deep learning methods have shown superior performance in capturing complex information in attribute graphs, and have been proven to be very effective methods for solving the problem of anomaly detection in attribute graphs. Firstly, a brief overview of common graph anomaly detection, attribute graph anomaly detection and representation learning related methods are given. Secondly, the latest deep learning anomaly detection methods are introduced and classified as static and dynamic attribute graphs. Finally, the application scenarios, existing problems and challenges of attribute graph anomaly detection are discussed, and the future research directions are prospected.
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    Summary of Application Research on Helmet Detection Algorithm Based on Deep Learning
    ZHANG Liyi, WU Wenhong, NIU Hengmao, SHI Bao, DUAN Kaibo, SU Chenyang
    Computer Engineering and Applications    2022, 58 (16): 1-17.   DOI: 10.3778/j.issn.1002-8331.2203-0580
    Abstract179)      PDF(pc) (967KB)(180)       Save
    Safety helmet is the most common and practical personal protective tool on the construction site, which can effectively prevent and reduce head injury caused by accidents. Helmet detection is the main work of personnel safety management on the construction site, and it is also an important content of intelligent monitoring technology on the construction site. With the development of deep learning, it has become an important part of smart site construction. In order to comprehensively analyze the research status of deep learning in helmet detection, aiming at the research of helmet detection algorithm, the commonly used helmet detection algorithm and helmet detection algorithm based on deep learning are summarized, and their advantages and disadvantages are explained in detail. On this basis, aiming at the existing problems, this paper systematically summarizes and analyzes the relevant improvement methods of helmet detection algorithm, and combs the characteristics, advantages and limitations of various methods. Finally, the future development direction of helmet detection algorithm based on deep learning is prospected.
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    Survey on Adversarial Example Attack and Defense Technology for Automatic Speech Recognition
    LI Kezi, XU Yang, ZHANG Sicong, YAN Jiale
    Computer Engineering and Applications    2022, 58 (14): 1-15.   DOI: 10.3778/j.issn.1002-8331.2202-0196
    Abstract176)      PDF(pc) (972KB)(128)       Save
    Speech recognition technology is an important way of human-computer interaction. With the continuous development of deep learning, automatic speech recognition system based on deep learning has also made important progress. However, well-designed audio adversarial examples can cause errors in the automatic speech recognition system based on neural network, and bring security risks to the application of combined speech recognition system. In order to improve the security of automatic speech recognition system based on neural network, it is necessary to study the attack and defense of audio adversarial examples. Firstly, the research status of adversarial examples generation and defense technology is analyzed and summarized. Then automatic speech recognition system audio adversarial examples attack and defense techniques and related challenges and solutions are introduced.
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    Image Semantic Segmentation Based on Fully Convolutional Neural Network
    ZHANG Xin, YAO Qing’an, ZHAO Jian, JIN Zhenjun, FENG Yuncong
    Computer Engineering and Applications    2022, 58 (8): 45-57.   DOI: 10.3778/j.issn.1002-8331.2109-0091
    Abstract175)      PDF(pc) (1057KB)(159)       Save
    Image semantic segmentation is a hot research topic in the field of computer vision. With the rapid rise of fully convolutional neural networks, the development of fusion of image semantic segmentation and fully convolutional networks has shown very bright results. Through the collection of high-quality literature in recent years, the focus is on the summary of full convolutional neural network image semantic segmentation methods. The collected literature is divided into classical semantic segmentation, real-time semantic segmentation and RGBD semantic segmentation according to the application scenarios, and then the representative segmentation methods are described. Commonly used public datasets and evaluation metrics for performance are also summarized, and experiments on commonly used datasets are analyzed and summarized. Finally, the possible future research directions of fully convolutional neural networks are prospected.
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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
    Abstract175)      PDF(pc) (4301KB)(195)       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.
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    Chinese Named Entity Recognition by Integrating Multi-Heads Attention Mechanism and Character and Words Fusion
    ZHAO Dandan, HUANG Degen, MENG Jiana, GU Feng, ZHANG Pan
    Computer Engineering and Applications    2022, 58 (7): 142-149.   DOI: 10.3778/j.issn.1002-8331.2104-0265
    Abstract174)      PDF(pc) (985KB)(93)       Save
    Named entity recognition(NER) is an important basic task in natural language processing and Chinese named entity recognition(CNER) is particularly difficult because of word segmentation ambiguity and polysemy. To solve these problems, a multi-heads attention mechanism(Multi-Attention) and character and words fusion CNER model is proposed. The model is abbreviated as CWA-CNER. Firstly, the character vector and its words vector are connected together. The words are the possible words containing the character in the sentence. Then the connected vector are input into bidirectional long short-term memory(BiLSTM) neural network to further extract contextual semantic information. Secondly, Multi-Attention is used to capture the tightness of the connection between elements in the sentence, and finally the entity labeling is carried out through conditional random field(CRF). The model is tested on Boson dataset, 1998 and 2014 People’s Daily corpus, and their F1 values are all more than 90%. The results show that the model is effective.
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    Survey of IoT Forensics
    LIANG Guangjun, XIN Jianfang, WANG Qun, NI Xueli, GUO Xiangmin, XIA Lingling
    Computer Engineering and Applications    2022, 58 (8): 12-32.   DOI: 10.3778/j.issn.1002-8331.2201-0014
    Abstract169)      PDF(pc) (1143KB)(159)       Save
    The advent of the Internet of Things era brings great convenience to people, but it also makes the scope of cyberspace attacks wider and brings new cyberspace security threats. Massive IoT devices retain a wealth of digital traces, which can provide insight into people’s various behaviors at home and other places. This is of great significance for digital forensics. This article has an in-depth discussion on IoT forensics, starting with the rise, development and research status of IoT forensics, and further discusses digital forensics models, 1-2-3 regional method models, parallel structure-IoT forensics models, privacy protection forensics models, and forensic model for special applications. Finally, it elaborates on the opportunities and challenges of forensics in the Internet of Things. This article strives to provide readers with help and reference for more in-depth research on the basis of systematically learning IoT forensics technology.
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    Review of Few-Shot Object Detection
    ZHANG Zhenwei, HAO Jianguo, HUANG Jian, PAN Chongyu
    Computer Engineering and Applications    2022, 58 (5): 1-11.   DOI: 10.3778/j.issn.1002-8331.2109-0405
    Abstract167)      PDF(pc) (1012KB)(163)       Save
    Recently, object detection based on deep learning has been achieved remarkable achievements and various of mature models have been proposed. However, most of these models rely on a large number of annotated training samples. Besides, in practical applications, it is often difficult to get access to large scale of high-quality annotated samples, which limits its application and popularization in specific areas. Few-shot object detection has been extensively researched taking advantage of its small dependence on the number of samples. Based on the current research, this paper reviews the current mainstream of the few-shot object detection systematically, including problem definition, mainstream methods, as well as common experimental designs. Then, it points out potential application directions. Furthermore, the generalized few-shot object detection is also briefly introduced. Finally, the paper analyzes challenges of the few-shot object detection technology and discusses corresponding countermeasures.
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    Research Progress Review of Hyperspectral Remote Sensing Image Band Selection
    YANG Hongyan, DU Jianmin
    Computer Engineering and Applications    2022, 58 (10): 1-12.   DOI: 10.3778/j.issn.1002-8331.2111-0403
    Abstract165)      PDF(pc) (776KB)(133)       Save
    Hyperspectral imaging remote sensing can obtain abundant spectral, radiation and spatial information of ground objects, which has been widely used in various fields of national economy. But its narrow band spacing brings not only rich spectral information, but also information redundancy and the difficulty of data processing. Therefore, before the practical application of hyperspectral remote sensing data, band selection is needed to extract spectral features and reduce the data dimension. This review summarizes the research progress of band selection for hyperspectral remote sensing images. Based on the analysis and summary of band selection strategies, the related technology and the latest research status are expounded from six aspects:the evaluation criteria of band selection, the band selection based on the combination of spatial and spectral features, the band selection based on semi-supervised learning, the band selection based on sparse representation, the band selection based on intelligent search and the band selection based on deep learning. Then, the current problems and challenges faced by hyperspectral image band selection are discussed. Finally, the future development direction of hyperspectral image band selection is predicted.
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    Knowledge-Aware Recommendation Algorithm Combined with Attention Mechanism
    ZHANG Xin, LIU Siyuan, XU Yanling
    Computer Engineering and Applications    2022, 58 (9): 168-174.   DOI: 10.3778/j.issn.1002-8331.2011-0054
    Abstract164)      PDF(pc) (604KB)(97)       Save
    The application of knowledge graphs in recommender systems has attracted more and more attention, which can effectively solve the data sparsity and cold start problems in recommender systems. However, when the existing path-based and embedded-based knowledge-aware recommendation algorithms merge entities in the knowledge graph to represent users, they do not consider that the importance of entities to users is not the same, and the recommendation results will be affected by unrelated entities. Aiming at the limitations of the existing methods, a new knowledge-aware recommendation algorithm combined with the attention mechanism is proposed, and an end-to-end framework for incorporating the knowledge graph into the recommendation system is given. From the user’s historical click items, multiple entity sets are expanded on the knowledge graph, and the user’s preference distribution is calculated through the attention mechanism, and the final click probability is predicted accordingly. Through comparative experiments with traditional recommendation algorithms on two real public data sets, the results show that this method has achieved significant improvement under the evaluation of multiple common indicators(such as AUC, ACC and Recall@top-K).
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    Complex Road Target Detection Algorithm Based on Improved YOLOv5
    WANG Pengfei, HUANG Hanming, WANG Mengqi
    Computer Engineering and Applications    2022, 58 (17): 81-92.   DOI: 10.3778/j.issn.1002-8331.2205-0158
    Abstract163)      PDF(pc) (1217KB)(161)       Save
    Aiming at the problem of false detection and missed detection caused by dense occluded targets and small targets in complex road background, a complex road target detection algorithm based on improved YOLOv5 is proposed. Firstly, Quality Focal Loss is introduced, which combines the classification score with the quality prediction of location to improve the positioning accuracy of dense occluded targets. Secondly, a shallow detection layer is added as the detection layer of smaller targets, the three-scale detection of the original algorithm is changed to four-scale detection, and the feature fusion part is also improved accordingly, which improves the learning ability of the algorithm to the features of small targets. Then, based on the feature fusion idea of weighted bidirectional feature pyramid network(BiFPN), a de-weighted BiFPN is proposed, which makes full use of deep, shallow and original feature information, strengthens feature fusion, reduces the loss of feature information in the process of convolution, and improves the detection accuracy. Finally, the convolution block attention module(CBAM) is introduced to further improve the feature extraction ability of the algorithm and make the algorithm pay more attention to useful information. The experimental results show that the detection accuracy of the improved algorithm in this paper on the public autopilot data set KITTI and the self-made rider helmet data set Helmet reaches 94.9% and 96.8% respectively, which is 1.9 percentage points and 2.1 percentage points higher than the original algorithm, and the detection speed reaches 69 FPS and 68 FPS respectively. It has better detection accuracy and real-time performance. At the same time, compared with some mainstream target detection algorithms, the improved algorithm in this paper also has some advantages.
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    New Research Advances in Facial Expression Recognition Under Partial Occlusion
    JIANG Bin, LI Nanxing, ZHONG Rui, WU Qinggang, CHANG Huawen
    Computer Engineering and Applications    2022, 58 (12): 12-24.   DOI: 10.3778/j.issn.1002-8331.2112-0268
    Abstract162)      PDF(pc) (937KB)(154)       Save
    With the development of artificial intelligence technology, facial expression recognition can extract expression states from images or videos to recognize the mental emotions of human beings, achieving a better human-computer interaction effect. However, there are many methods only focusing on facial expression recognition without occlusion, which is not applicable to objective and complex scenarios and greatly limits the practicality of the algorithm. In recent years, aiming at different types of occlusion such as illumination occlusion, low resolution occlusion, pose variations, and physical occlusion, researchers have proposed various new methods to solve these problems. The survey introduces the core ideas of relevant methods, and conducts a comparison analysis about these methods, meanwhile looks forward to future research and development.
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    Algorithm for Portrait Segmentation Combined with MobileNetv2 and Attention Mechanism
    WANG Xin, WANG Meili, BIAN Dangwei
    Computer Engineering and Applications    2022, 58 (7): 220-228.   DOI: 10.3778/j.issn.1002-8331.2106-0334
    Abstract162)      PDF(pc) (1064KB)(295)       Save
    As for low precision and efficiency in portrait segmentation, an algorithm for portrait segmentation combined with MobileNetv2 and attention mechanism is proposed to achieve the portrait segmentation. With keeping the encoder-decoder of U-typed network , MobileNetv2 is used as the backbone of the network and streamline the upsampling process, it can reduce the parameters of the network. It is helpful for transfer and network training. The network with attention mechanism can learn portrait features more effectively, and the mixed loss is beneficial to the classification of difficult pixels of portrait edges. A portrait bust can be selected as the input of the model, and the corresponding image mask can be produced by the network. The proposed algorithm is tested on Human_Matting dataset and EG1800 dataset. The results show that the accuracy of the proposed algorithm is 98.3%(Matting) and 97.8%(EG1800), which is higher than PortraitNet(96.3%(Matting) and 95.8%(EG1800)) and DeepLabv3+(96.8%(Matting) and 96.4%(EG1800)). The algorithm can clearly separate the target person from the background. The proposed algorithm’s IOU can reach to 98.6%(Matting) and 98.2%(EG1800), which can be used in lightweight applications and provides a new research idea for portrait segmentation.
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    Computer Engineering and Applications    2022, 58 (8): 0-0.  
    Abstract159)      PDF(pc) (637KB)(142)       Save
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    Review of Research on Approximate Reinforcement Learning Algorithms
    SI Yanna, PU Jiexin, SUN Lifan
    Computer Engineering and Applications    2022, 58 (8): 33-44.   DOI: 10.3778/j.issn.1002-8331.2112-0082
    Abstract155)      PDF(pc) (678KB)(104)       Save
    Reinforcement learning(RL) is one of the most important techniques for artificial intelligence(AI). However, traditional tabular reinforcement learning is difficult to deal with control problems with large scale or continuous space. Approximate reinforcement learning is inspired by the idea of function approximation to parameterize the value function or strategy function, and obtains the optimal strategy indirectly through parameter optimization. It has been widely used in video games, Go game, robot control, etc. and obtained remarkable performance. In view of this, this paper reviews the research status and application progress of approximate reinforcement learning algorithms. Firstly, the basic theory of approximate reinforcement learning is introduced. Then the classical algorithms of approximate reinforcement learning are classified and expounded, including some corresponding improvement methods. Finally, the research progress of approximate reinforcement learning in robotics is summarized, and some major problems are summarized to provide reference for future research.
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    Traffic Monitoring Video Vehicle Volume Statistics Method Based on Improved YOLOv5s+DeepSORT
    LI Yongshang, MA Ronggui, ZHANG Meiyue
    Computer Engineering and Applications    2022, 58 (5): 271-279.   DOI: 10.3778/j.issn.1002-8331.2108-0346
    Abstract155)      PDF(pc) (6574KB)(102)       Save
    To address the problem of low accuracy of vehicle volume statistics based on traffic monitoring video, an improved YOLOv5s detector combined with Deep SORT method is proposed.In order to improve the detection rate, the attention module CBAM is integrated with the Neck of the YOLOv5s to improve the feature extraction ability. CIoU Loss is used as the target bounding box regression loss function instead of GIoU Loss to speed up the bounding box regression rate while increasing positioning accuracy. NMS is replaced by DIoU-NMS to reduce the fail of detection when the targets are crowded. The structure of appearance feature extraction network of Deep SORT is refined, and it is retrained on the vehicle re-identification dataset to reduce identity switch caused by target occlusion. The improved YOLOv5s detector is fused with Deep SORT, and a virtual detection line is set in the video to count the traffic flow. The results show that the improved YOLOv5s has an average accuracy of 2.3 percentage points higher than that of the original algorithm. Combined with Deep SORT, the statistical accuracy of traffic flow in off-peak, rush hour, and night scenarios reaches 93.5%, 91.2%, and 89.9%.
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    Computer Engineering and Applications    2022, 58 (6): 0-0.  
    Abstract155)      PDF(pc) (636KB)(97)       Save
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    Survey of Quantum Swarm Intelligence Optimization Algorithm
    AN Jiale, LIU Xiaonan, HE Ming, SONG Huichao
    Computer Engineering and Applications    2022, 58 (7): 31-42.   DOI: 10.3778/j.issn.1002-8331.2110-0141
    Abstract154)      PDF(pc) (976KB)(89)       Save
    With the continuous development of science and technology, optimization theory and its derived algorithms have been widely used in people’s daily work and life, especially in the real world, many problems can be described as combinatorial optimization problems. In recent years, swarm intelligence optimization algorithm has been proved to be effective in solving combinatorial optimization problems. Introducing the concept of quantum computing into swarm intelligence optimization algorithm, a new research direction is opened for better solving combinatorial optimization problems. In the past 20 years, many quantum swarm optimization algorithms have been developed, and more people have improved and applied them. This paper summarizes quantum ant colony algorithm, quantum particle swarm algorithm, quantum artificial fish swarm algorithm, quantum bee colony optimization algorithm, quantum cuckoo search algorithm, quantum hybrid frog leaping algorithm, quantum firefly algorithm, quantum bat algorithm and other quantum swarm Intelligent optimization algorithms. The future problems and research directions of quantum swarm optimization algorithm are discussed.
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