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Review on Integration Analysis and Application of Multi-omics Data ZHONG Yating, LIN Yanmei, CHEN Dingjia, PENG Yuzhong, ZENG Yuanpeng Computer Engineering and Applications    2021, 57 (23): 1-17.   DOI: 10.3778/j.issn.1002-8331.2106-0341 Abstract （1521）      PDF（pc） （806KB）（1775）       Knowledge map    Save With the continuous emergence and popularization of new omics sequencing technology, a large number of omics data have been produced, which is of great significance for people to further study and reveal the mysteries of life. Using multi-omics data to integrate and analyze life science problems can obtain more abundant and more comprehensive information related to life system, which has become a new direction for scientists to explore the mechanism of life. This paper introduces the research background and significance of multi-omics data integration analysis, summarizes the methods of data integration analysis of multiomics in recent years and the applied research in related fields, and finally discusses the current existing problems and future prospects of multi-omics data integration analysis methods. Reference | Related Articles | Metrics

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Review of Fault Diagnosis Techniques for UAV Flight Control Systems AN Xue, LI Shaobo, ZHANG Yizong, ZHANG Ansi Computer Engineering and Applications    2023, 59 (24): 1-15.   DOI: 10.3778/j.issn.1002-8331.2305-0137 Abstract （389）      PDF（pc） （917KB）（1468）       Knowledge map    Save In recent years, unmanned aerial vehicles（UAVs） have been widely used in various complex fields of military and civilian applications due to their unique advantages such as low operating costs and high mobility. At the same time, the complex and diverse missions have put forward higher requirements for the reliability and safety of UAV systems. The UAV fault diagnosis technology can provide timely and accurate diagnosis results, which helps the maintenance, repair and servicing of UAVs, and is of great significance in enhancing the combat effectiveness of UAVs. Therefore, this paper firstly analyses UAV flight control systems, and classifies the faults. Secondly, the research methods and status quo of UAV fault diagnosis technology are analysed and summarised. Finally, the main challenges faced by UAV fault diagnosis technology are discussed and the future development direction is pointed out; the aim is to provide some reference for researchers in the field of UAV fault diagnosis technology and to promote the improvement of UAV fault diagnosis technology level in China. Reference | Related Articles | Metrics

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Overview on Reinforcement Learning of Multi-agent Game WANG Jun, CAO Lei, CHEN Xiliang, LAI Jun, ZHANG Legui Computer Engineering and Applications    2021, 57 (21): 1-13.   DOI: 10.3778/j.issn.1002-8331.2104-0432 Abstract （1324）      PDF（pc） （779KB）（1377）       Knowledge map    Save The use of deep reinforcement learning to solve single-agent tasks has made breakthrough progress. Since the complexity of multi-agent systems, common algorithms cannot solve the main difficulties. At the same time, due to the increase in the number of agents, taking the expected value of maximizing the cumulative return of a single agent as the learning goal often fails to converge and some special convergence points do not satisfy the rationality of the strategy. For practical problems that there is no optimal solution, the reinforcement learning algorithm is even more helpless. The introduction of game theory into reinforcement learning can solve the interrelationship of agents very well and explain the rationality of the strategy corresponding to the convergence point. More importantly, it can use the equilibrium solution to replace the optimal solution in order to obtain a relatively effective strategy. Therefore, this article investigates the reinforcement learning algorithms that have emerged in recent years from the perspective of game theory, summarizes the important and difficult points of current game reinforcement learning algorithms and gives several breakthrough directions that may solve the above-mentioned difficulties. Reference | Related Articles | Metrics

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Review of Attention Mechanism in Convolutional Neural Networks ZHANG Chenjia, ZHU Lei, YU Lu Computer Engineering and Applications    2021, 57 (20): 64-72.   DOI: 10.3778/j.issn.1002-8331.2105-0135 Abstract （2031）      PDF（pc） （973KB）（1301）       Knowledge map    Save Attention mechanism is widely used in deep learning tasks because of its excellent effect and plug and play convenience. This paper mainly focuses on convolution neural network, introduces various mainstream methods in the development process of convolution network attention mechanism, extracts and summarizes its core idea and implementation process, realizes each attention mechanism method, and makes comparative experiments and results analysis on the measured data of the same type of emitter equipment. According to the main ideas and experimental results, the research status and future development direction of attention mechanism in convolutional networks are summarized. Reference | Related Articles | Metrics

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Progress on Deep Reinforcement Learning in Path Planning ZHANG Rongxia, WU Changxu, SUN Tongchao, ZHAO Zengshun Computer Engineering and Applications    2021, 57 (19): 44-56.   DOI: 10.3778/j.issn.1002-8331.2104-0369 Abstract （2994）      PDF（pc） （1134KB）（1243）       Knowledge map    Save The purpose of path planning is to allow the robot to avoid obstacles and quickly plan the shortest path during the movement. Having analyzed the advantages and disadvantages of the reinforcement learning based path planning algorithm, the paper derives a typical deep reinforcement learning, Deep Q-learning Network（DQN） algorithm that can perform excellent path planning in a complex dynamic environment. Firstly, the basic principles and limitations of the DQN algorithm are analyzed in depth, and the advantages and disadvantages of various DQN variant algorithms are compared from four aspects：the training algorithm, the neural network structure, the learning mechanism and AC（Actor-Critic） framework. The paper puts forward the current challenges and problems to be solved in the path planning method based on deep reinforcement learning. The future development directions are proposed, which can provide reference for the development of intelligent path planning and autonomous driving. Reference | Related Articles | Metrics

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Review of Research on Generative Adversarial Networks and Its Application WEI Fuqiang, Gulanbaier Tuerhong, Mairidan Wushouer Computer Engineering and Applications    2021, 57 (19): 18-31.   DOI: 10.3778/j.issn.1002-8331.2104-0248 Abstract （824）      PDF（pc） （1078KB）（1182）       Knowledge map    Save The theoretical research and applications of generative adversarial networks have been continuously successful and have become one of the current hot spots of research in the field of deep learning. This paper provides a systematic review of the theory of generative adversarial networks and their applications in terms of types of models, evaluation criteria and theoretical research progress; analyzing the strengths and weaknesses of generative models with explicit and implicit density-based, respectively; summarizing the evaluation criteria of generative adversarial networks, interpreting the relationship between the criteria, and introduces the research progress of the generative adversarial network in image generation from the application level, that is, through the image conversion, image generation, image restoration, video generation, text generation and image super-resolution applications; analyzing the theoretical research progress of generative adversarial networks from the perspectives of interpretability, controllability, stability and model evaluation methods. Finally, the paper discusses the challenges of studying generative adversarial networks and looks forward to the possible future directions of development. 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 （1523）      PDF（pc） （787KB）（1117）       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 Image Quality Assessment Method Based on Deep Learning CAO Yudong, LIU Haiyan, JIA Xu, LI Xiaohui Computer Engineering and Applications    2021, 57 (23): 27-36.   DOI: 10.3778/j.issn.1002-8331.2106-0228 Abstract （980）      PDF（pc） （646KB）（991）       Knowledge map    Save Image quality evaluation is a measurement of the visual quality of an image or video. The researches on image quality evaluation algorithms in the past 10 years are reviewed. First, the measurement indicators of image quality evaluation algorithm and image quality evaluation datasets are introduced. Then, the different classification of image quality evaluation methods are analyzed, and image quality evaluation algorithms with deep learning technology are focused on, basic model of which is deep convolutional network, deep generative adversarial network and transformer. The performance of algorithms with deep learning is often higher than that of traditional image quality assessment algorithms. Subsequently, the principle of image quality assessment with deep learning is described in detail. A specific no-reference image quality evaluation algorithm based on deep generative adversarial network is introduced, which improves the reliability of simulated reference images through enhanced confrontation learning. Deep learning technology requires massive data support. Data enhancement methods are elaborated to improve the performance of the model. Finally, the future research trend of digital image quality evaluation is summarized. Reference | Related Articles | Metrics

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Review of Application of Transfer Learning in Medical Image Field GAO Shuang, XU Qiaozhi Computer Engineering and Applications    2021, 57 (24): 39-50.   DOI: 10.3778/j.issn.1002-8331.2107-0300 Abstract （791）      PDF（pc） （896KB）（975）       Knowledge map    Save Deep learning technology has developed rapidly and achieved significant results in the field of medical image treatment. However, due to the small number of medical image samples and difficult annotation, the effect of deep learning is far from reaching the expectation. In recent years, using transfer learning method to alleviate the problem of insufficient medical image samples and improve the effect of deep learning technology in the field of medical image has become one of the research hotspots. This paper first introduces the basic concepts, types, common strategies and models of transfer learning methods, then combs and summarizes the representative related research in the field of medical images according to the types of transfer learning methods, and finally summarizes and prospects the future development of this field. 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 （1438）      PDF（pc） （1089KB）（923）       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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Improved Lightweight Attention Model Based on CBAM FU Guodong, HUANG Jin, YANG Tao, ZHENG Siyu Computer Engineering and Applications    2021, 57 (20): 150-156.   DOI: 10.3778/j.issn.1002-8331.2101-0369 Abstract （2761）      PDF（pc） （808KB）（874）       Knowledge map    Save In recent years, the attention model has been widely used in the field of computer vision. By adding the attention module to the convolutional neural network, the performance of the network can be significantly improved. However, most of the existing methods focus on the development of more complex attention modules to enable the convolutional neural network to obtain stronger feature expression capabilities, but this also inevitably increases the complexity of the model. In order to achieve a balance between performance and complexity, a lightweight EAM（Efficient Attention Module） model is proposed to optimize the CBAM model. For the channel attention module of CBAM, one-dimensional convolution is introduced to replace the fully connected layer to aggregate the channels. For the spatial attention module of CBAM, the large convolution kernel is replaced with a dilated convolution to increase the receptive field for aggregation Broader spatial context information. After integrating the model into YOLOv4 and testing it on the VOC2012 data set, mAP is increased by 3.48 percentage points. Experimental results show that the attention model only introduces a small amount of parameters, and the network performance can be greatly improved. Reference | Related Articles | Metrics

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Summary of Dynamic Gesture Recognition Based on Vision XIE Yinggang, WANG Quan Computer Engineering and Applications    2021, 57 (22): 68-77.   DOI: 10.3778/j.issn.1002-8331.2105-0314 Abstract （742）      PDF（pc） （598KB）（810）       Knowledge map    Save Gestures have played a very important role in human communication since ancient times, and the visual dynamic gesture identification technology is to use new technologies such as computer vision and IOT（Internet of Things） perception, and 3D visual sensors, allowing the machine to understand human gestures, thus making humanity and machine more good communication, because of far-reaching research significance for human-machine interaction. The sensor techniques used in dynamic gesture identification are introduced, and the technical parameters of the related sensors are compared. By tracking the dynamic gesture recognition technology of vision at home and abroad, the processing process of dynamic gesture recognition is first stated：gesture detection and segmentation, gesture tracking, gesture classification. By comparing the methods involved in each process, it can be seen that deep learning has strong fault tolerance, robustness, high parallelism, anti-interference, etc., which has achieved great achievements above the traditional learning algorithm in the field of gesture identification. Finally, the challenges currently encountering and the future possible development of dynamic gesture identification are analyzed. 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 （1392）      PDF（pc） （1009KB）（807）       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 （1372）      PDF（pc） （1381KB）（763）       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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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 （1168）      PDF（pc） （1285KB）（760）       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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Review of Neural Style Transfer Models TANG Renwei, LIU Qihe, TAN Hao Computer Engineering and Applications    2021, 57 (19): 32-43.   DOI: 10.3778/j.issn.1002-8331.2105-0296 Abstract （1163）      PDF（pc） （1078KB）（741）       Knowledge map    Save Neural Style Transfer（NST） technique is used to simulate different art styles of images and videos, which is a popular topic in computer vision. This paper aims to provide a comprehensive overview of the current progress towards NST. Firstly, the paper reviews the Non-Photorealistic Rendering（NPR） technique and traditional texture transfer. Then, the paper categorizes current major NST methods and gives a detailed description of these methods along with their subsequent improvements. After that, it discusses various applications of NST and presents several evaluation methods which compares different style transfer models both qualitatively and quantitatively. In the end, it summarizes the existing problems and provides some future research directions for NST. 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 （845）      PDF（pc） （666KB）（706）       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 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 （1164）      PDF（pc） （875KB）（701）       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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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 （1462）      PDF（pc） （1013KB）（679）       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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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 （1093）      PDF（pc） （662KB）（678）       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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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 （893）      PDF（pc） （4301KB）（676）       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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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 （1616）      PDF（pc） （1109KB）（652）       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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Cauchy Kernel-Based Density Peaks Clustering Algorithm for Categorical Data SHENG Jinchao, DU Mingjing, LI Yurui, SUN Jiarui Computer Engineering and Applications    2022, 58 (18): 162-171.   DOI: 10.3778/j.issn.1002-8331.2201-0440 Abstract （166）      PDF（pc） （968KB）（639）       Knowledge map    Save The density peak clustering algorithm has difficulty in producing better clustering results when dealing with categorical data. To address this phenomenon, the article analyzes in detail the reasons for its generation：the overlap problem of distance calculation and the aggregation problem of density calculation. To address the above problems, this article proposes a density peak clustering algorithm for categorical data, referred to as CDPCD. The algorithm points out the ordinal feature （the order relationship between attribute values of categorical data） that rarely exists in the current categorical data distance metric process, and then proposes a weighted ordered distance measure based on probability distribution to alleviate the overlap problem. The data density values are re-evaluated by combining the method of the Cauchy kernel function on a shared nearest neighbor density peak clustering algorithm with improved density calculation and quadratic assignment, which enhances the density diversity and reduces the impact caused by the aggregation problem. Experimental results on several real datasets show that CDPCD achieves better clustering results compared to traditional division-based and density-based clustering algorithms. Reference | Related Articles | Metrics

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Survey of Research on Neural Network Verification and Testing Technology LI Duo, DONG Chaoqun, SI Pinchao, HE Man, LIU Qianchao Computer Engineering and Applications    2021, 57 (22): 53-67.   DOI: 10.3778/j.issn.1002-8331.2106-0342 Abstract （310）      PDF（pc） （811KB）（639）       Knowledge map    Save Neural network technology has made remarkable achievements in the fields of image processing, text analysis and speech recognition. With the application of neural network technology to some security related fields, how to ensure the quality of these software applications is particularly important. Software based on neural network technology is essentially different from traditional software in development and programming. Traditional testing technology is difficult to be directly applied to this kind of software. It is necessary to study the verification and testing evaluation technology for neural network. To effectively evaluate and test neural networks, this paper reviews the research status of neural network verification and testing technology, summarizes and classifies the verification technology, testing technology based on coverage, testing technology based on adversarial sample, and fusing traditional testing technology. The basic idea and implementation of some key technologies are briefly introduced, and some testing frameworks and tools are listed. The challenges of neural network verification and testing are summarized, which can provide reference for researchers in this field. Reference | Related Articles | Metrics

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Survey on Zero-Shot Learning WANG Zeshen，YANG Yun，XIANG Hongxin, LIU Qing Computer Engineering and Applications    2021, 57 (19): 1-17.   DOI: 10.3778/j.issn.1002-8331.2106-0133 Abstract （1039）      PDF（pc） （1267KB）（631）       Knowledge map    Save Although there have been well developed in zero-shot learning since the development of deep learning, in the aspect of the application, zero-shot learning did not have a good system to order it. This paper overviews theoretical systems of zero-shot learning, typical models, application systems, present challenges and future research directions. Firstly, it introduces the theoretical systems from definition of zero-shot learning, essential problems, and commonly used data sets. Secondly, some typical models of zero-shot learning are described in chronological order. Thirdly, it presents the application systems about of zero-shot learning from the three dimensions, such as words, images and videos. Finally, the paper analyzes the challenges and future research directions in zero-shot learning. 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 （1295）      PDF（pc） （605KB）（627）       Knowledge map    Save As an important branch of intelligent transportation system, road traffic flow prediction plays an important role in congestion prediction, path planning. The spatio-temporal polymorphism and complex correlation of road traffic flow data force the transformation and upgrading of road traffic flow prediction methods in the era of big data. In order to mine the time-space characteristics of traffic flow, scholars have proposed various methods, including model fusion, model algorithm improvement, data definition conversion, etc, in order to improve the prediction accuracy of the model. In order to reasonably summarize all kinds of traffic flow prediction methods, they are divided into three categories according to the types of methods used：statistics based methods, machine learning based methods, and depth learning based methods. This paper summarizes and analyzes the new models and algorithms in recent years by summarizing various traffic flow prediction methods, aiming to provide research ideas for relevant researchers. Finally, the methods of traffic flow prediction are summarized and prospected, and the exploration direction of the future traffic flow prediction field is given. Reference | Related Articles | Metrics

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Review on Semantic Segmentation of UAV Aerial Images CHENG Qing, FAN Man, LI Yandong, ZHAO Yuan, LI Chenglong Computer Engineering and Applications    2021, 57 (19): 57-69.   DOI: 10.3778/j.issn.1002-8331.2105-0423 Abstract （866）      PDF（pc） （926KB）（625）       Knowledge map    Save With the rapid development of Unmanned Aerial Vehicle（UAV） technology, research institutions and industries have attached importance of UAV’s application. Optical images and videos are vital for the UAV to sense the environment, occupying an important position in UAV vision. As a hot spot of the current research of computer vision, semantic segmentation is widely investigated in the fields of unmanned driving and intelligent robot. Semantic segmentation of UAV aerial images is based on the UAV aerial image semantic segmentation technology to enable the UAV to work in complex scenes. First of all, a brief introduction to the semantic segmentation technology and the application development of UAV is given. Meanwhile, the relevant UAV aerial data sets, characteristics of aerial images and commonly used evaluation metrics for semantic segmentation are introduced. Secondly, according to the characteristics of UAV aerial images, it introduces the relevant semantic segmentation methods. In this section, analysis and comparison are made in three aspects including the small object detection, the real-time performance of the models and the multi-scale information integration. Finally, the related applications of semantic segmentation for UAV are reviewed, including line detection, the application of agriculture and building extraction, and analysis of the development trend and challenges in the future is made. Reference | Related Articles | Metrics

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Survey on Deep Learning Based Image Super-Resolution XIA Hao, LYU Hongfeng, LUO Jun, CAI Nian Computer Engineering and Applications    2021, 57 (24): 51-60.   DOI: 10.3778/j.issn.1002-8331.2105-0418 Abstract （898）      PDF（pc） （914KB）（616）       Knowledge map    Save Image super-resolution reconstruction is the process of using low-resolution images to reconstruct the corresponding high-resolution images. At present, image super-resolution technology has been successfully applied in the fields of computer vision and image processing. In recent years, due to deep learning’s ability of self-learning from a large amount of data, it has been widely used in the field of image super-resolution. This article introduces the background of image super-resolution reconstruction, and summarizes the deep learning based image super-resolution model in detail, and then elaborates the image super-resolution technology in satellite remote sensing images, medical imaging, video surveillance, and industrial inspection tasks application. Finally, this article summarizes the current research status and future development directions of image super-resolution algorithms. 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 （869）      PDF（pc） （6521KB）（613）       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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Detection Method of Illegal Building Based on YOLOv5 YU Juan，LUO Shun Computer Engineering and Applications    2021, 57 (20): 236-244.   DOI: 10.3778/j.issn.1002-8331.2106-0178 Abstract （514）      PDF（pc） （1653KB）（583）       Knowledge map    Save Aiming at solving the problem of slow detection rate and high false detection rate caused by the illegal buildings in the UAV images, which are mostly small targets and partially occluded targets, a detection method of illegal buildings based on YOLOv5 network is proposed. Firstly, at the beginning and end of original batch normalization module, centering and scaling calibration are added to enhance the effective features and form a more stable feature distribution, which strengthens the feature extraction ability of network model. Then, the smoothed KL（Kullback-Leibler） divergence loss function is adopted to replace the cross entropy in the confidence of original loss function, which further improves the generalization performance of model. Finally, the backbone feature extraction network of YOLOv5 is improved, and the residual module is replaced with the LSandGlass module to reduce information loss and eliminate low-resolution feature layers to reduce semantic loss. Experimental results show that the training of the proposed improved model is easier to make network converge in comparison with original YOLOv5, and the speed of detecting illegal buildings has been greatly improved, and then detection accuracy has been improved. 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 （925）      PDF（pc） （683KB）（579）       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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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 （868）      PDF（pc） （854KB）（575）       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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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 （1087）      PDF（pc） （995KB）（557）       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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Application of Deep Reinforcement Learning Algorithm on Intelligent Military Decision System KUANG Liqun, LI Siyuan, FENG Li, HAN Xie, XU Qingyu Computer Engineering and Applications    2021, 57 (20): 271-278.   DOI: 10.3778/j.issn.1002-8331.2104-0114 Abstract （901）      PDF（pc） （1223KB）（554）       Knowledge map    Save Deep reinforcement learning algorithm can well achieve discrete decision-making behavior, but it is difficult to apply to the highly complex and continuous modern battlefield situations, and the algorithm is difficult to converge in multi-agent environment. To solve these problems, an improved Deep Deterministic Policy Gradient（DDPG） algorithm is proposed, which introduces the experience replay technology based on priority and single training mode to improve the convergence speed of the algorithm; at the same time, an exploration strategy of mixed double noise is designed in the algorithm to realize complex and continuous military decision-making and control behavior. The intelligent military decision simulation platform based on the improved DDPG algorithm is developed by unity3D. The simulation environment of Blue Army Infantry attacking Red Army military base is built to simulate multi-agent combat training. The experimental results show that the algorithm can drive multiple combat agents to complete tactical maneuvers and achieve tactical behaviors, such as bypassing obstacles to reach the dominant area for shooting. The algorithm has faster convergence speed and better stability. It can get higher round rewards, and achieves the purpose of improving the efficiency of intelligent military decision-making. Reference | Related Articles | Metrics

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Multi-channel Attention Mechanism Text Classification Model Based on CNN and LSTM TENG Jinbao, KONG Weiwei, TIAN Qiaoxin, WANG Zhaoqian, LI Long Computer Engineering and Applications    2021, 57 (23): 154-162.   DOI: 10.3778/j.issn.1002-8331.2104-0212 Abstract （1587）      PDF（pc） （844KB）（548）       Knowledge map    Save Aiming at the problem that traditional Convolutional Neural Network（CNN） and Long Short-Term Memory （LSTM） can not reflect the importance of each word in the text when extracting features, this paper proposes a multi-channel text classification model based on CNN and LSTM. Firstly, CNN and LSTM are used to extract the local information and context features of the text; secondly, multi-channel attention mechanism is used to extract the attention score of the output information of CNN and LSTM; finally, the output information of multi-channel attention mechanism is fused to achieve the effective extraction of text features and focus attention on important words. Experimental results on three public datasets show that the proposed model is better than CNN, LSTM and their improved models, and can effectively improve the effect of text classification. 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 （1044）      PDF（pc） （782KB）（544）       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 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 （998）      PDF（pc） （1036KB）（542）       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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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 （1021）      PDF（pc） （921KB）（540）       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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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 （649）      PDF（pc） （755KB）（537）       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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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 （798）      PDF（pc） （808KB）（510）       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