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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 （1526）      PDF（pc） （787KB）（1122）       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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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 （1402）      PDF（pc） （1009KB）（813）       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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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 （1298）      PDF（pc） （605KB）（631）       Knowledge map    Save As an important branch of intelligent transportation system, road traffic flow prediction plays an important role in congestion prediction, path planning. The spatio-temporal polymorphism and complex correlation of road traffic flow data force the transformation and upgrading of road traffic flow prediction methods in the era of big data. In order to mine the time-space characteristics of traffic flow, scholars have proposed various methods, including model fusion, model algorithm improvement, data definition conversion, etc, in order to improve the prediction accuracy of the model. In order to reasonably summarize all kinds of traffic flow prediction methods, they are divided into three categories according to the types of methods used：statistics based methods, machine learning based methods, and depth learning based methods. This paper summarizes and analyzes the new models and algorithms in recent years by summarizing various traffic flow prediction methods, aiming to provide research ideas for relevant researchers. Finally, the methods of traffic flow prediction are summarized and prospected, and the exploration direction of the future traffic flow prediction field is given. Reference | Related Articles | Metrics

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Survey of Transformer 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 （1173）      PDF（pc） （1285KB）（765）       Knowledge map    Save Transformer is a deep neural network based on self-attention mechanism. In recent years, Transformer-based models have become a hot research direction in the field of computer vision, and their structures are constantly being improved and expanded, such as local attention mechanisms, pyramid structures, and so on. Through the improved vision model based on Transformer structure, the performance optimization and structure improvement are reviewed and summarized respectively. In addition，the advantages and disadvantages of the respective structures of the Transformer and convolutional neural network（CNN） are compared and analyzed，and a new hybrid structure of CNN+Transformer is introduced. Finally，the development of Transformer in computer vision is summarized and prospected. Reference | Related Articles | Metrics

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Survey of 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 （1171）      PDF（pc） （875KB）（704）       Knowledge map    Save Transformer is a kind of deep learning framework with strong modeling and parallel computing capabilities. At present, object detection algorithm based on Transformer has become a hotspot. In order to further explore new ideas and directions, this paper summarizes the existing object detection algorithm based on Transformer as well as a variety of object detection data sets and their application scenarios. This paper describes the correlation algorithms for Transformer based object detection from four aspects, i.e. feature extraction, object estimation, label matching policy and application of algorithm, compares the Transformer algorithm with the object detection algorithm based on convolutional neural network, analyzes the advantages and disadvantages of Transformer in object detection task, and proposes a general framework for Transformer based object detection model. Finally, the prospect of development trend of Transformer in the field of object detection is put forward. Reference | Related Articles | Metrics

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Review 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 （1099）      PDF（pc） （662KB）（685）       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 of Camera Pose Estimation Methods Based on Deep Learning WANG Jing, JIN Yuchu, GUO Ping, HU Shaoyi Computer Engineering and Applications    2023, 59 (7): 1-14.   DOI: 10.3778/j.issn.1002-8331.2209-0280 Abstract （1069）      PDF（pc） （702KB）（509）       Knowledge map    Save Camera pose estimation is a technology to accurately estimate the 6-DOF position and pose of camera in world coordinate system under known environment. It is a key technology in robotics and automatic driving. With the rapid development of deep learning, using deep learning to optimize camera pose estimation algorithm has become one of the current research hotspots. In order to master the current research status and trends of camera pose estimation algorithms, the mainstream algorithms based on deep learning are summarized. Firstly, the traditional camera pose estimation methods based on feature points is briefly introduced. Then, the camera pose estimation method based on deep learning is mainly introduced. According to the different core algorithms, the end-to-end camera pose estimation, scene coordinate regression, camera pose estimation based on retrieval, hierarchical structure, multi-information fusion and cross scenescamera pose estimation are elaborated and analyzed in detail. Finally, this paper summarizes the current research status, points out the challenges in the field of camera pose estimation based on in-depth performance analysis, and prospects the development trend of camera pose estimation. Reference | Related Articles | Metrics

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Overview of 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）（547）       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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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 （1025）      PDF（pc） （921KB）（549）       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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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 （1006）      PDF（pc） （1036KB）（546）       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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Review of Research on Driver Fatigue Driving Detection Methods ZHANG Rui, ZHU Tianjun, ZOU Zhiliang, SONG Rui Computer Engineering and Applications    2022, 58 (21): 53-66.   DOI: 10.3778/j.issn.1002-8331.2204-0053 Abstract （934）      PDF（pc） （946KB）（425）       Knowledge map    Save The proportion of traffic accidents caused by fatigue driving has increased year by year, which has attracted widespread attention from researchers. At present, the research of fatigue driving testing is limited by various factors such as scientific and technological level, environment, and road, which makes it difficult to further develop fatigue driving detection technology. This article introduces the latest progress in driver fatigue driving detection methods in the past decade. The two categories of active detection method and passive detection method are elaborated and reviewed. According to the different characteristics of the two major types of detection methods, it is carefully classified. The advantages and limitations of various fatigue driving detection methods are further analyzed, and the detection algorithms used in the active detection method based on facial features in the past three years are analyzed and summarized. Finally, the shortcomings of various fatigue driving detection methods are summarized, and the future research trends in the field of fatigue detection are proposed, which provides new ideas for researchers to further research. Reference | Related Articles | Metrics

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Review of Explainable Artificial Intelligence ZHAO Yanyu, ZHAO Xiaoyong, WANG Lei, WANG Ningning Computer Engineering and Applications    2023, 59 (14): 1-14.   DOI: 10.3778/j.issn.1002-8331.2208-0322 Abstract （926）      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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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 （897）      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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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 （882）      PDF（pc） （854KB）（585）       Knowledge map    Save Simultaneous localization and mapping (SLAM) is a crucial technology for autonomous mobile robots and autonomous driving systems, with a laser scanner (also known as lidar) playing a vital role as a supporting sensor for SLAM algorithms. This article provides a comprehensive review of lidar-based SLAM algorithms. Firstly, it introduces the overall framework of lidar-based SLAM, providing detailed explanations of the functions of the front-end odometry, back-end optimization, loop closure detection, and map building modules, along with a summary of the algorithms used. Secondly, it presents descriptions and summaries of representative open-source algorithms in a sequential order of 2D to 3D and single-sensor to multi-sensor fusion. Additionally, it discusses commonly used open-source datasets, precision evaluation metrics, and evaluation tools. Lastly, it offers an outlook on the development trends of lidar-based SLAM technology from four dimensions: deep learning, multi-sensor fusion, multi-robot collaboration, and robustness research. Reference | Related Articles | Metrics

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Survey of 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 （878）      PDF（pc） （6521KB）（624）       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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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 （848）      PDF（pc） （666KB）（709）       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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Improved YOLOv7-tiny’s Object Detection Lightweight Model LIU Haohan, FAN Yiming, HE Huaiqing, HUI Kanghua Computer Engineering and Applications    2023, 59 (14): 166-175.   DOI: 10.3778/j.issn.1002-8331.2302-0115 Abstract （808）      PDF（pc） （830KB）（315）       Knowledge map    Save At present, the object detection algorithm has a large number of parameters and high computational complexity. However, the storage capacity and computing power of mobile terminals are limited and it is difficult to deploy it. So in this paper, it proposes the improved YOLOv7-tiny for mobile terminal devices. An efficient backbone network and a lightweight feature fusion network are further proposed with the ShuffleNet v1-improved and EALN-GS as the basic building units. The combination of the two part can reduce computational complexity, obtain more rich semantic information, and further improve detection accuracy. The Mish activation function is used to increase nonlinear expression and improve the generalization ability of the model. Experimental results show that compared with the original model, the accuracy of the improved model is improved by 3.3%, the number of parameters and calculations are reduced by 4.8% and 13.7%, and the model scale is reduced by 8.7%. The improved YOLOv7-tiny reduces the amount of parameters and calculations of the model while maintaining high accuracy, further improves the detection effect, and provides feasibility for deployment in edge terminal devices. Reference | Related Articles | Metrics

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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 （805）      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

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Construction and Application of Discipline Knowledge Graph in Personalized Learning ZHAO Yubo, ZHANG Liping, YAN Sheng, HOU Min, GAO Mao Computer Engineering and Applications    2023, 59 (10): 1-21.   DOI: 10.3778/j.issn.1002-8331.2209-0345 Abstract （769）      PDF（pc） （929KB）（482）       Knowledge map    Save The discipline knowledge graph is an important tool to support teaching activities based on big data, artificial intelligence and other technologies. As a kind of discipline knowledge semantic network, it contributes to the development of personalized learning systems and the promotion of new infrastructure for digital education resources. Firstly, this paper outlines the concept and classification of knowledge graph. Secondly, this paper summarizes the concept, characteristics, advantages, connotation and the support for personalized learning of discipline knowledge graph. Nextly, this paper focuses on the sorting of construction process of discipline knowledge graph：discipline ontology construction, discipline knowledge extraction, discipline knowledge fusion and discipline knowledge processing, and it also introduces the application of discipline knowledge graph in personalized learning situations and the challenges. Finally, this paper prospects the future tendency of discipline knowledge graph and personalized learning. It provides the reference and inspiration for the organization of educational resources and the innovative development of personalized learning. Reference | Related Articles | Metrics

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Multi-Modal Meteorological Forecasting Based on Transformer XIANG Deping, ZHANG Pu, XIANG Shiming, PAN Chunhong Computer Engineering and Applications    2023, 59 (10): 94-103.   DOI: 10.3778/j.issn.1002-8331.2208-0486 Abstract （734）      PDF（pc） （977KB）（488）       Knowledge map    Save Thanks to the rapid development of meteorological observation technology, the meteorological industry has accumulated massive meteorological data, which provides an opportunity to build new data-driven meteorological forecasting methods. Due to the long-term dependence and large-scale spatial correlation hidden in meteorological data, and due to the complex coupling relationship between different modalities, meteorological forecasting with deep learning is still a challenging research topic. This paper presents a deep learning model for meteorological forecasting based on multi-modal fusion, using sequential multi-modal data in same atmospheric pressure levels composed of four classical meteorological elements：temperature, relative humidity, U-component of wind and V-component of wind. Specifically, convolutional network is used to learn features from every modality, and with those features, the gating mechanism is introduced to multi-modal weighted fusion. Secondly, the attention mechanism is introduced, which replaces the traditional attention mechanism with parallel spatial-temporal axial attention, in order to effectively learn long-term dependencies and large-scale spatial associations. Architecturally, the Transformer encoder-decoder structure is employed as the overall framework. Extensive comparative experiments have been conducted on the regional ERA5 reanalysis dataset, demonstrating that the proposed method is effective and superior in the prediction of temperature, relative humidity and wind. Reference | Related Articles | Metrics

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Research Progress in Application of Graph Anomaly Detection in Financial Anti-Fraud LIU Hualing, LIU Yaxin, XU Junyi, CHEN Shanghui, QIAO Liang Computer Engineering and Applications    2022, 58 (22): 41-53.   DOI: 10.3778/j.issn.1002-8331.2203-0233 Abstract （724）      PDF（pc） （1848KB）（504）       Knowledge map    Save With the rapid development of digital finance, fraud presents new characteristics such as intellectualization, industrialization and strong concealment. And the limitations of traditional expert rules and machine learning methods are increa- singly apparent. Graph anomaly detection technology has a strong ability to deal with associated information, which provides new idea for financial anti-fraud. Firstly, the development and advantages of graph anomaly detection are briefly introduced. Secondly, from the perspectives of individual anti-fraud and group anti-fraud, graph anomaly detection technology is divided into individual fraud detections based on feature, proximity, graph representation learning or community division, and gang fraud detections based on dense subgraph, dense subtensor or deep network structure. The basic idea, advantages, disadvantages, research progress and typical applications of each anomaly detection technology are analyzed and compared. Finally, the common test data sets and evaluation criteria are summarized, and the development prospect and research direction of graph anomaly detection technology in financial anti-fraud are given. Reference | Related Articles | Metrics

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Research on 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 （652）      PDF（pc） （755KB）（541）       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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Overview of Cross-Modal Retrieval Technology XU Wenwan, ZHOU Xiaoping, WANG Jia Computer Engineering and Applications    2022, 58 (23): 12-23.   DOI: 10.3778/j.issn.1002-8331.2205-0160 Abstract （648）      PDF（pc） （769KB）（246）       Knowledge map    Save Cross modal retrieval can retrieve the information of other models through one model, which has become a research hot-spot in the era of big data. Researchers based on real value representation and binary representation to reduce the semantic gap of different modal information and compare the similarity effectively, but there will still be the problem of low retrieval efficiency or information loss. At present, how to further improve retrieval efficiency and information utilization is a key challenge for cross modal retrieval research. Firstly, the development status of real value representation and binary representation in cross-modal retrieval is introduced. Secondly, it analyzes and compares five cross modal retrieval methods based on modeling technology and similarity comparison under two presentation technologies：subspace learning, topic statistical model learning, deep learning, traditional hash and deep hash. Then, the latest multi-modal datasets are summarized to provide valuable reference for relevant researchers and engineers. Finally, the challenges of cross modal retrieval are analyzed and the future research directions in this field are pointed out. Reference | Related Articles | Metrics

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Survey of Sentiment Analysis Algorithms Based on Multimodal Fusion GUO Xu, Mairidan Wushouer, Gulanbaier Tuerhong Computer Engineering and Applications    2024, 60 (2): 1-18.   DOI: 10.3778/j.issn.1002-8331.2305-0439 Abstract （647）      PDF（pc） （954KB）（476）       Knowledge map    Save Sentiment analysis is an emerging technology that aims to explore people’s attitudes toward entities and can be applied to various domains and scenarios, such as product evaluation analysis, public opinion analysis, mental health analysis and risk assessment. Traditional sentiment analysis models focus on text content, yet some special forms of expression, such as sarcasm and hyperbole, are difficult to detect through text. As technology continues to advance, people can now express their opinions and feelings through multiple channels such as audio, images and videos, so sentiment analysis is shifting to multimodality, which brings new opportunities for sentiment analysis. Multimodal sentiment analysis contains rich visual and auditory information in addition to textual information, and the implied sentiment polarity (positive, neutral, negative) can be inferred more accurately using fusion analysis. The main challenge of multimodal sentiment analysis is the integration of cross-modal sentiment information; therefore, this paper focuses on the framework and characteristics of different fusion methods and describes the popular fusion algorithms in recent years, and discusses the current multimodal sentiment analysis in small sample scenarios, in addition to the current development status, common datasets, feature extraction algorithms, application areas and challenges. It is expected that this review will help researchers understand the current state of research in the field of multimodal sentiment analysis and be inspired to develop more effective models. Reference | Related Articles | Metrics

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Survey on Credit Card Transaction Fraud Detection Based on Machine Learning JIANG Hongxun, JIANG Junyi, LIANG Xun Computer Engineering and Applications    2023, 59 (21): 1-25.   DOI: 10.3778/j.issn.1002-8331.2302-0129 Abstract （614）      PDF（pc） （674KB）（385）       Knowledge map    Save Machine learning has its distinctiveness in credit card transaction detection and faces a more complex environment. Since the intervention of human intelligence, machine learning encounters harder challenges in fraud detection than the ones of face recognition and driverlessness, which leads to failures if only applying the processes of engineering disciplines. This paper depicts the 2000-since research history of credit card anti-fraud; identifies the definition, scope, technical streams, applications, and other key concepts, and their interconnections in the field of detection oriented machine learning; analyzes the general architecture of fraud detection and summarizes the state-of-the-art of transaction fraud detection research in terms of feature engineering, models/algorithms, and evaluation metrics; discusses various detection algorithms of credit card transaction fraud and enumerates their original intention, core ideas, solution methods, advantages or disadvantages, and relevant extensions; highlights unsupervised, supervised, and semi-supervised learning models of fraud recognition, as well as various ensembles such as models cascading and aggregation; addresses three major challenges, i.e., massive data, sample skew, and concept drift, and compiles the latest progresses to alleviate these problems. This paper concludes with the limitations, controversies, and challenges of machine learning on credit card fraud recognition, and provides the trend analysis and suggestions for future research directions. Reference | Related Articles | Metrics

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Survey on Emotion Recognition in Conversation CHEN Xiaoting, LI Shi Computer Engineering and Applications    2023, 59 (3): 33-48.   DOI: 10.3778/j.issn.1002-8331.2207-0417 Abstract （603）      PDF（pc） （681KB）（328）       Knowledge map    Save Emotion recognition in conversation（ERC） is a hot research topic in the field of emotion computing, which aims to detect the emotion category of each discourse during the dialogue. It has important research significance for dialogue understanding and dialogue generation. At the same time, it has a wide range of practical application value in many fields, such as social media analysis, recommendation system, medical treatment and human-computer interaction. With the continuous innovation and development of deep learning technology, emotion recognition in conversation has attracted more and more attention from academia and industry. At this stage, it is necessary to summarize these research results in an overview article in order to better carry out follow-up work. The research results in this field are comprehensively sorted out from the perspectives of problem definition, problem approach, research methods, and mainstream datasets, and the development of dialogue emotion recognition tasks is reviewed and analyzed. Compared with video and audio, dialogue text contains more information. Therefore, this paper focuses on combing the text dialogue emotion recognition methods, especially the methods based on deep learning. Finally, based on the current research status, this paper summarizes the open problems existing in the field of dialogue emotion recognition and the development trend in the future. Reference | Related Articles | Metrics

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Review of Path Planning Algorithms for Robot Navigation CUI Wei, ZHU Fazheng Computer Engineering and Applications    2023, 59 (19): 10-20.   DOI: 10.3778/j.issn.1002-8331.2301-0088 Abstract （600）      PDF（pc） （595KB）（325）       Knowledge map    Save Path planning is one of the key technologies for robot navigation. An excellent path planning algorithm can quickly find the best collision-free path and improve operational efficiency. Most existing classification methods have difficulty in expressing the differences and connections between algorithms. To distinguish different path planning algorithms more clearly, they are divided into graph-based search, bionic-based, potential field-based, velocity space-based and sampling-based algorithms based on their principle and nature. This paper introduces the concept, characteristics, and development status of each type of algorithm, analyzes the more widely used sample-based algorithms from the perspective of single-query and multi-query algorithms, and the advantages and problems of different types of path planning algorithms are compared and summarized. Finally, the future development trend of robot path planning algorithms in terms of multi-robot collaboration, multi-algorithm fusion and adaptive planning is prospected. Reference | Related Articles | Metrics

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Survey of Evaluation Metrics and Methods for Semantic Segmentation YU Ying, WANG Chunping, FU Qiang, KOU Renke, WU Weiyi, LIU Tianyong Computer Engineering and Applications    2023, 59 (6): 57-69.   DOI: 10.3778/j.issn.1002-8331.2207-0139 Abstract （598）      PDF（pc） （740KB）（286）       Knowledge map    Save Deep learning has made major breakthroughs in the field of semantic segmentation. Standard, well-known and comprehensive metrics should be used to evaluate the performance of these algorithms to ensure objectivity and effectiveness of the evaluation. Through summary of the existing semantic segmentation evaluation metrics, this paper elaborates from some aspects, e.g., pixel accuracy, depth estimation error metric, operation efficiency, memory demand and robustness. Especially, the widely used accuracy metrics such as F1 score, mIoU, mPA, Dice coefficient and Hausdorff distance are introduced in detail. In addition, this paper expounds the related research on the robustness and generalization. Furthermore, this paper points out the requirements in the semantic segmentation experiment and the limitations of segmentation quality evaluation. Reference | Related Articles | Metrics

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Review of Research on Application of Vision Transformer in Medical Image Analysis SHI Lei, JI Qingyu, CHEN Qingwei, ZHAO Hengyi, ZHANG Junxing Computer Engineering and Applications    2023, 59 (8): 41-55.   DOI: 10.3778/j.issn.1002-8331.2206-0022 Abstract （590）      PDF（pc） （869KB）（365）       Knowledge map    Save Deep self-attentive network（Transformer） has a natural ability to model global features and long-range correlations of input information, which is strongly complementary to the inductive bias property of convolutional neural networks（CNN）. Inspired by its great success in natural language processing, Transformer has been widely introduced into various computer vision tasks, especially medical image analysis, and has achieved remarkable performance. In this paper, it first introduces the typical work of vision Transformer on natural images, and then organizes and summarizes the related work according to different lesions or organs in the subfields of medical image segmentation, medical image classification and medical image registration, focusing on the implementation ideas of some representative work. Finally, current researches are discussed and the future direction is pointed out. The purpose of this paper is to provide a reference for further in-depth research in this field. Reference | Related Articles | Metrics

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Review of Cross-Domain Object Detection Algorithms Based on Depth Domain Adaptation LIU Hualing, PI Changpeng, ZHAO Chenyu, QIAO Liang Computer Engineering and Applications    2023, 59 (8): 1-12.   DOI: 10.3778/j.issn.1002-8331.2210-0063 Abstract （580）      PDF（pc） （583KB）（392）       Knowledge map    Save In recent years, the object detection algorithm based on deep learning has attracted wide attention due to its high detection performance. It has been successfully applied in many fields such as automatic driving and human-computer interaction and has achieved certain achievements. However, traditional deep learning methods are based on the assumption that the training set （source domain） and the test set （target domain） follow the same distribution, but this assumption is not realistic, which severely reduces the generalization performance of the model. How to align the distribution of the source domain and the target domain so as to improve the generalization of the object detection model has become a research hotspot in the past two years. This article reviews cross-domain object detection algorithms. First, it introduces the preliminary knowledge of cross-domain object detection：depth domain adaptation and object detection. The cross-domain object detection is decomposed into two small areas for an overview, in order to understand its development from the bottom logic. In turn, this article introduces the latest developments in cross-domain object detection algorithms, from the perspectives of differences, confrontation, reconstruction, hybrid and other five categories, and sorts out the research context of each category. Finally, this article summarizes and looks forward to the development trend of cross-domain object detection algorithms. Reference | Related Articles | Metrics

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Review of Deep Learning Methods for MRI Reconstruction DENG Gewen, WEI Guohui, MA Zhiqing Computer Engineering and Applications    2023, 59 (20): 67-76.   DOI: 10.3778/j.issn.1002-8331.2302-0057 Abstract （569）      PDF（pc） （580KB）（308）       Knowledge map    Save Magnetic resonance imaging（MRI） is a commonly used imaging technique in the clinic, but the excessive imaging time limits its further development. Image reconstruction from undersampled k-space data has been an important part of accelerating MRI imaging. In recent years, deep learning has shown great potential in MRI reconstruction, and its reconstruction results and efficiency are better than traditional compressed sensing methods. To sort out and summarize the current deep learning-based MRI reconstruction methods, it firstly introduces the definition of MRI reconstruction problem, secondly analyzes the application of deep learning in data-driven end-to-end reconstruction and model-driven unrolled optimization reconstruction, then provides evaluation metrics and common datasets for reconstruction, and finally discusses the challenges faced by current MRI reconstruction and future research directions. Reference | Related Articles | Metrics

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Review of Data Normalization Methods YANG Hanyu, ZHAO Xiaoyong, WANG Lei Computer Engineering and Applications    2023, 59 (3): 13-22.   DOI: 10.3778/j.issn.1002-8331.2207-0179 Abstract （561）      PDF（pc） （707KB）（260）       Knowledge map    Save In recent years, artificial intelligence has been widely used in various fields and has achieved remarkable results. Data normalization is a significant part of the implementation of artificial intelligence applications, which helps avoid incorrect modeling of data by neural networks due to the complexity of data dimensions. In the big data scenario, a portion of the data arrives at the training points successively in the form of streams. As a result, the research on data normalization in the stream scenario is a core problem that needs to be solved urgently. Currently, there are many reviews on normalization research, most of which only focus on the normalization research of batch data, but lack a summary of normalization methods for stream data, which is not informative. This paper systematically and exhaustively analyzes the literature on stream data normalization based on batch data normalization, condenses and proposes a normalization classification method based on stream data, and classifies the data normalization methods into batch data normalization methods and stream data normalization methods. At the same time, this paper compares and analyzes the principles, advantages, and main problems that can be solved by these methods. Finally, the future research directions of data normalization in different scenarios are prospected. Reference | Related Articles | Metrics

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Research on Emotion Recognition Based on EEG Time-Frequency-Spatial Multi-Domain Feature Fusion WANG Lu, LIANG Mingjing, SHI Huiyu, WEN Xin, CAO Rui Computer Engineering and Applications    2023, 59 (4): 191-196.   DOI: 10.3778/j.issn.1002-8331.2109-0083 Abstract （557）      PDF（pc） （642KB）（233）       Knowledge map    Save The traditional emotion recognition based on electroencephalogram（EEG） mainly adopted a single EEG feature extraction approach. In order to make full use of the rich information contained in EEG, a new method of EEG emotion recognition based on multi-domain feature fusion is proposed. This paper extracts EEG features in time-domain, frequency-domain and space-domain, and fuses the three domain features as the input of the emotion recognition model. Firstly, the power spectral density of the three frequency bands of alpha, beta and gamma of the EEG signal in different time windows are calculated, and combined with the spatial information of the EEG electrode, the EEG images are formed. Then, the convolutional neural network（CNN） and bidirectional long short-term memory network（BLSTM） are used to construct the CNN-BLSTM model for emotion recognition, and the features of time, frequency and space domains are learned respectively. The method is verified in the SEED dataset. The results show that the method can effectively improve the accuracy of recognition, and the average recognition accuracy is 96.25%. Reference | Related Articles | Metrics

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Graph Neural Network and Its Research Progress in Field of Image Processing JIANG Yuying, CHEN Xinyu, LI Guangming, WANG Fei, GE Hongyi Computer Engineering and Applications    2023, 59 (7): 15-30.   DOI: 10.3778/j.issn.1002-8331.2205-0503 Abstract （524）      PDF（pc） （659KB）（261）       Knowledge map    Save Graph neural network （GNN） is a deep learning-based model for processing graph-structured data, which has received much attention from researchers for its good interpretability and powerful nonlinear fitting ability to graph-structured data. With the rise of GNN, GNN has been developed to integrate with image processing techniques and has made breakthroughs in image classification, human body analysis and visual quizzing. Firstly, image processing techniques and the theory of traditional neural networks are introduced, and the principles, characteristics and shortcomings of five major classes of GNNs are analyzed. Secondly, the applications of GNN in the image processing field from five technical levels are analyzed respectively, and the representative models of each class of methods are listed. Thirdly, the common models described in the paper are compared and summarized from the perspective of both datasets and performance evaluation metrics, and nine common public datasets in image processing are introduced in addition. Finally, areas for improvement in GNN in the field of image processingare analyzed in depth, and the prospects of its application in the field of image processing are presented. Reference | Related Articles | Metrics

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Review of Object Detection Algorithm Improvement in Deep Learning YANG Feng, DING Zhitong, XING Mengmeng, DING Bo Computer Engineering and Applications    2023, 59 (11): 1-15.   DOI: 10.3778/j.issn.1002-8331.2209-0312 Abstract （522）      PDF（pc） （691KB）（364）       Knowledge map    Save Object detection is currently a research hotspot in the field of computer vision. With the development of deep learning, object detection algorithms based on deep learning are increasingly applied and their performance is constantly improved. This paper summarizes the latest research progress of object detection methods based on deep learning by summarizing common problems encountered in the process of object detection and corresponding improvement methods. This paper focuses on two types of object detection algorithms based on deep learning. In addition, the latest improvement ideas of target detection algorithms are summarized from the aspects of attention mechanism, lightweight network, multi-scale detection. Finally, in view of the current problems in the field of target detection, the future development trend is prospected. And the feasible solution is put forward in order to provide reference ideas and directions for the follow-up research work in this field. Reference | Related Articles | Metrics

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Lightweight Masked Face Recognition Algorithm Incorporating Attention Mechanism YE Zixun, ZHANG Hongying, HE Yujun Computer Engineering and Applications    2023, 59 (3): 166-174.   DOI: 10.3778/j.issn.1002-8331.2206-0222 Abstract （520）      PDF（pc） （707KB）（109）       Knowledge map    Save The globalization pandemic of COVID-19 has made wearing masks to become a norm in people’s lives, and this preventive measure brings new challenges to face recognition algorithms. To address this problem, this paper proposes a lightweight masked face recognition algorithm. Firstly, this network introduces GhostNet as the backbone feature extraction network to improve the recognition speed. Secondly a FocusNet feature enhancement extraction network incorporating spatial attention mechanism is proposed to make the model focus on the upper half of the face region that are not covered by masks. Then, in order to overcome the problem of inadequate masked face dataset, a data augmentation method using 3D face mesh is proposed to add face mask. Finally, the experimental results show that, compared with the benchmark model, the proposed model reduces the number of model parameters by 84%, while the AP of masked face recognition increases by 4.29?percentage points, which better balances speed and accuracy. Reference | Related Articles | Metrics

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Survey of Research Methods for Low Light Image Enhancement PENG Daxin, ZHEN Tong, LI Zhihui Computer Engineering and Applications    2023, 59 (18): 14-27.   DOI: 10.3778/j.issn.1002-8331.2210-0143 Abstract （519）      PDF（pc） （645KB）（296）       Knowledge map    Save The purpose of low-light image enhancement is to restore images with complete details in low-light conditions, and it has gradually become a hot spot in computer image processing research. The quality of image imaging is crucial to intelligent security, video surveillance, and other scenarios and has a very broad application prospect in related industries. In order to study low-light image enhancement in depth, firstly, the traditional low-light image enhancement methods are classified and analyzed in detail, and then the image enhancement methods based on deep learning are listed, and the various networks used and the problems solved are detailed and compared the mentioned methods in detail. Then, the data set is analyzed and studied in detail, and some commonly used evaluation indicators are briefly sorted out. Finally, it summarizes the content, points out the difficulties in the current research, and points out the research goals for the future. Reference | Related Articles | Metrics

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Review of Recommendation Systems Using Knowledge Graph ZHANG Mingxing, ZHANG Xiaoxiong, LIU Shanshan, TIAN Hao, YANG Qinqin Computer Engineering and Applications    2023, 59 (4): 30-42.   DOI: 10.3778/j.issn.1002-8331.2209-0033 Abstract （510）      PDF（pc） （702KB）（318）       Knowledge map    Save With the rapid development of the Internet, how to obtain the needed information from huge amounts of data becomes more important. The recommendation system is a method of screening information, which aims to recommend personalized content for users. However, traditional recommendation algorithms still suffer from several challenges, such as data sparsity and cold start. In recent years, researchers have used the rich entity and relationship information in the knowledge graph to alleviate the above problems. The overall performance of the recommendation system is enhanced. This paper gives a review of the recommendation system based on knowledge graph from three aspects：Firstly, basic concepts of the recommendation system and knowledge graph are introduced. The shortcomings of the existing recommendation algorithms are pointed out. Then, the research of the recommendation system based on knowledge graph is analyzed in detail. The advantages and challenges of the different approaches are assessed. Finally, relevant application scenarios and future development prospects are summarized. Reference | Related Articles | Metrics

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Review of Depression Detection Using Social Media Text Data XU Dongdong, CAI Xiaohong, LIU Jing, CAO Hui Computer Engineering and Applications    2023, 59 (4): 54-63.   DOI: 10.3778/j.issn.1002-8331.2209-0042 Abstract （510）      PDF（pc） （598KB）（272）       Knowledge map    Save Machine learning has been gradually applied to depression detection using social media text data, and has prominently shown important application value in recent years. Firstly, this paper organizes and classifies social media text datasets, data preprocessing and machine learning methods used for depression detection. In addition, in terms of data feature representation, the basic feature representation, static and contextual word embedding are compared and analyzed. Secondly, this paper analyzes comprehensively the performance and characteristics of traditional machine learning with different basic features and different algorithm types as well as deep learning for depression detection. Finally, this paper summarizes and suggests further explorations in Chinese dataset creation, model interpretability, metaphor-based detection and lightweight pre-training model. Reference | Related Articles | Metrics

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Review of Application of Machine Learning in Radiomics Analysis LU Huimin, XUE Han, WANG Yilong, WANG Guizeng, SANG Pengcheng Computer Engineering and Applications    2023, 59 (17): 22-34.   DOI: 10.3778/j.issn.1002-8331.2210-0435 Abstract （509）      PDF（pc） （5275KB）（284）       Knowledge map    Save Radiomics is a technique for quantitatively extracting features from standard medical images. The construction of predictive or diagnostic models with the assistance of machine learning enables data to be extracted and applied in clinical decision support systems to improve diagnostic accuracy, which has been widely used in tumor staging, cancer detection, survival analysis and other tasks. The application and research progress of machine learning in radiomics analysis are reviewed. The applicability and limitations of machine learning algorithms in each stage of radiomics analysis are emphatically discussed, and the representative algorithms are thoroughly sorted out and analyzed in terms of principles and application effects. The evaluation methods to the work of each stage in the radiomics analysis are comprehensively introduced. The publicly available medical image datasets and software toolkits for radiomics feature extraction are organized. Finally, the future development of machine learning in radiomics is discussed. Reference | Related Articles | Metrics