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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
    Abstract1364)      PDF(pc) (787KB)(956)       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.
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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
    Abstract953)      PDF(pc) (875KB)(542)       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.
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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
    Abstract864)      PDF(pc) (1009KB)(533)       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.
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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
    Abstract859)      PDF(pc) (605KB)(394)       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.
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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
    Abstract683)      PDF(pc) (808KB)(453)       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.
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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
    Abstract641)      PDF(pc) (830KB)(238)       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.
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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
    Abstract625)      PDF(pc) (929KB)(404)       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.
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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
    Abstract609)      PDF(pc) (683KB)(403)       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.
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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
    Abstract558)      PDF(pc) (662KB)(351)       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.
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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
    Abstract525)      PDF(pc) (977KB)(352)       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.
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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
    Abstract459)      PDF(pc) (691KB)(319)       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.
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    Target Detection Algorithm of Remote Sensing Image Based on Improved YOLOv5
    LI Kunya, OU Ou, LIU Guangbin, YU Zefeng, LI Lin
    Computer Engineering and Applications    2023, 59 (9): 207-214.   DOI: 10.3778/j.issn.1002-8331.2209-0119
    Abstract458)      PDF(pc) (665KB)(323)       Save
    Aiming at the problems of low target detection accuracy caused by high background complexity, multiple target sizes and too many small targets in remote sensing images, this paper proposes a target detection algorithm of remote sensing image based on improved YOLOv5. The channel-global attention mechanism(CGAM) is introduced into the backbone network to enhance the feature extraction ability of targets at different scales and to suppress the interference of redundant information. The dense upsampling convolution(DUC) module is introduced to expand the low resolution convolution feature maps, which can effectively enhance the fusion effect of different convolution feature maps. The improved algorithm is applied to the open remote sensing data set RSOD, and the average accuracy AP value of the improved YOLOv5 algorithm reaches 78.5%, which is 3.1?percentage points higher than that of the original algorithm. Experimental results show that the improved algorithm can effectively improve the accuracy of remote sensing image target detection.
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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
    Abstract420)      PDF(pc) (674KB)(285)       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.
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    FS-YOLOv5:Lightweight Infrared Rode Target Detection Method
    HUANG Lei, YANG Yuan, YANG Chengyu, YANG Wei, LI Yaohua
    Computer Engineering and Applications    2023, 59 (9): 215-224.   DOI: 10.3778/j.issn.1002-8331.2210-0487
    Abstract397)      PDF(pc) (815KB)(271)       Save
    In order to solve the problems of traditional target recognition algorithm in complex scene, including low precision, poor real-time performance and difficulty in small target detection, an FS-YOLOv5s lightweight model based on infrared scene is proposed. A new FS-MobileNetV3 network is proposed to extract feature images instead of CSPDarknet backbone network, which is based on YOLOv5s, a one-stage target detection network. Based on the CIOU loss function of the original network, a Power transform is introduced, which is replaced by α-CIoU to improve the detection ability of the network to small targets. Then K-means++ clustering algorithm is applied to the FLIR infrared data set to regenerate the Anchor. DIoU-NMS is used to replace the NMS post-processing method of the original network to improve the detection ability of occluded objects and reduce the missed detection rate of the model. The ablation experiments on the FLIR infrared dataset have verified that the FS-YOLOv5s lightweight algorithm can meet the task of road target detection in infrared scenes. Compared with the original network, the average accuracy of the FS-YOLOv5s model is only reduced by 0.37?percentage points. The size is reduced by 26%, the number of parameters is reduced by 29%, and the detection speed is increased by 11?FPS, which meets the needs of mobile deployment in different scenarios.
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    Survey of Fully Cooperative Multi-Agent Deep Reinforcement Learning
    ZHAO Liyang, CHANG Tianqing, CHU Kaixuan, GUO Libin, ZHANG Lei
    Computer Engineering and Applications    2023, 59 (12): 14-27.   DOI: 10.3778/j.issn.1002-8331.2209-0186
    Abstract392)      PDF(pc) (661KB)(256)       Save
    As one of the important branches in the field of machine learning and artificial intelligence, fully cooperative multi-agent deep reinforcement learning effectively combines the expression and decision-making ability of deep reinforcement learning with the distributed cooperation ability of multi-agent system in a general way, which provides an end-to-end solution to the model-free sequential decision-making problem in fully cooperative multi-agent system. Firstly, the basic principles of deep reinforcement learning are described, and the development of single agent deep reinforcement learning is summarized from three main directions:value function based, policy gradient based and actor-critic based. Secondly, the main challenges and training framework of multi-agent deep reinforcement learning are analyzed. Then, according to the different ways of realizing the maximum team joint reward, the fully cooperative multi-agent deep reinforcement learning is divided into four categories:independent learning, communication learning, collaborative learning and reward function shaping. Finally, from the perspective of solving practical problems, the future development direction of fully cooperative multi-agent deep reinforcement learning algorithm is prospected.
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    Improved Yolov7-tiny Algorithm for Steel Surface Defect Detection
    QI Xiangming, DONG Xu
    Computer Engineering and Applications    2023, 59 (12): 176-183.   DOI: 10.3778/j.issn.1002-8331.2302-0191
    Abstract386)      PDF(pc) (679KB)(227)       Save
    In order to improve the efficiency of small target detection of steel surface defects, an improved Yolov7-tiny steel surface defect detection algorithm is proposed. The activation function of the feature extraction network is changed  to SiLU to improve the feature extraction capability. The tensor splicing operation of the feature fusion network is combined with the weighted bidirectional feature pyramid BiFPN, and the nearest interpolation of the upper sampling part is replaced with the lightweight operator CARAFE to improve the feature fusion ability. Finally, the multi-head self-attention mechanism MHSA and SPD convolution building blocks are introduced at the output end to improve the detection performance of the output end for small targets of steel surface defects. The ablation and contrast experiments are carried out on the NEU-DET dataset. Compared with the original Yolov7-tiny algorithm, the improved algorithm has increased the mAP by 11.7 percentage points, the precision by 3.3 percentage points, and the FPS value reaches 192. The results show that the improved algorithm can effectively improve the detection efficiency of small targets of steel surface defects. Comparative experiments on the VOC2012 dataset show that the improved algorithm is universal.
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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
    Abstract384)      PDF(pc) (854KB)(251)       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.
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    Causal Attention Transformer Model for Stock Price Prediction
    REN Jiayi, WANG Aiyin
    Computer Engineering and Applications    2023, 59 (13): 325-334.   DOI: 10.3778/j.issn.1002-8331.2212-0127
    Abstract382)      PDF(pc) (654KB)(160)       Save
    Stock price prediction is a key topic of common concern for financial research and quantitative investment, and the use of deep learning techniques to reveal the market patterns of stock markets has become a hot research topic in recent years. Most of the existing deep learning models for stock price prediction only study point-in-time data, and this structural shortcoming causes them to fail to reflect the impact of the cumulative effect of feature factors on stock prices. To address this, a Transformer-based stock price forecasting model Stockformer is proposed by redesigning the model to handle time series data. it mines the time-series dependence between stock prices and feature factors through a causal self-attentiveness mechanism, employs a trend enhancement module to provide the model with the trend features of the series, and uses encoder-specific inputs to provide the prediction direct a priori information of the input features. The experimental results show that the prediction accuracy of Stockformer is significantly better than that of existing deep learning models, and the average absolute error and root mean square error are reduced by 23.2% and 25.7%, respectively, compared with the classical Transformer prediction model, and the predicted values are more suitable to the real values; and the ablation experiments are conducted to evaluate the causal attention mechanism of Stockformer, the effects of the time-series feature extraction means and specific model inputs are evaluated by ablation experiments respectively, and the superiority and generalizability of the proposed model are verified.
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    LSTFormer:Lightweight Semantic Segmentation Network Based on Swin Transformer
    YANG Cheng, GAO Jianlin, ZHENG Meilin, DING Rong
    Computer Engineering and Applications    2023, 59 (12): 166-175.   DOI: 10.3778/j.issn.1002-8331.2210-0331
    Abstract381)      PDF(pc) (801KB)(226)       Save
    Aiming at the general problem of high computational complexity in existing semantic segmentation networks based on Transformer, a lightweight semantic segmentation network based on Swin Transformer is proposed. Firstly, feature maps of multiple scales are obtained by Swin Transformer. Secondly, the full perception module and the improved cascading fusion module are used to fuse the feature maps of different scales across layers, reducing the semantic gap between the feature maps of different levels. Then, a single Swin Transformer block is introduced to optimize the initial segmentation feature mapping and improve the ability of the network to classify different pixels through the moving window autoattention mechanism. Finally, Dice loss function and cross-entropy loss function are added in the training stage to improve the segmentation performance and convergence speed of the network. The experimental results show that the mIoU of LSTFormer on ADE20K and Cityscapes reaches 49.47% and 81.47%. Compared with similar networks such as SETR and Swin-UPerNet, LSTFormer has lower parameters and computation while maintaining the same segmentation accuracy.
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    Survey on Computational Approaches for Drug-Target Interaction Prediction
    ZHANG Ran, WANG Xuezhi, WANG Jiajia, MENG Zhen
    Computer Engineering and Applications    2023, 59 (12): 1-13.   DOI: 10.3778/j.issn.1002-8331.2210-0108
    Abstract375)      PDF(pc) (675KB)(257)       Save
    Drug-target interaction prediction aims to discover potential drugs acting on specific proteins, and plays an important role in drug?repositioning, drug side effect prediction, polypharmacology and drug resistance research. With the advancement of computer processing and the continuous updating of computing algorithms, the computational drug-target interaction prediction has shown the advantages of short time, low cost, high precision and wide range, which has received extensive attention and made remarkable progress. In order to sort out the development history and explore the future research direction, the background and significance of drug-target interaction prediction are firstly introduced in brief. Secondly, the methods are classified into four types:molecular docking-based, drug structure-based, text mining-based and chemogenomic-based methods. A comparative analysis of each method is carried out, and the data requirements and application scenarios for each type of methods are described in detail. Finally, the limitations and challenges of the existing research are discussed, and the future research directions are prospected to provide references for follow-up research.
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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
    Abstract375)      PDF(pc) (580KB)(213)       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.
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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
    Abstract370)      PDF(pc) (595KB)(215)       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.
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    Review of Small Object Detection Algorithms Based on Deep Learning
    DONG Gang, XIE Weicheng, HUANG Xiaolong, QIAO Yitian, MAO Qian
    Computer Engineering and Applications    2023, 59 (11): 16-27.   DOI: 10.3778/j.issn.1002-8331.2211-0377
    Abstract365)      PDF(pc) (646KB)(228)       Save
    The existing object detection algorithms have high accuracy for the detection of large objects and medium objects, but due to the few pixels in the image and the available features of small objects, the detection accuracy of small objects is too low compared with that of large objects. By fusing the feature layer, the detection of small objects has achieved good results, but there are still problems such as the localization of small objects. Based on this, the definition of small objects is first explained, and five reasons for the low detection accuracy of small objects are pointed out. Subsequently, the latest progress in recent years and the classic small object detection optimization method in the past are described from multi-scale features, novel metric, and super-resolution according to the general principle. Secondly, the detection methods of small objects for specific scenes:aerial images, faces, and pedestrians are summarized. Finally, the possible research directions of small object detection in the future are summarized and proposed.
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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
    Abstract365)      PDF(pc) (954KB)(253)       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.
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    Image Inpainting Algorithm Based on Deep Neural Networks
    LYU Jianfeng, SHAO Lizhen, LEI Xuemei
    Computer Engineering and Applications    2023, 59 (20): 1-12.   DOI: 10.3778/j.issn.1002-8331.2303-0111
    Abstract364)      PDF(pc) (720KB)(380)       Save
    With the rapid development of deep learning, computer vision technology is applied more and more widely. At the same time, the image inpainting technology based on the known information of the damaged image using deep neural network has also become a hot topic. The image inpainting methods based on depth neural network in recent years are reviewed and analyzed. Firstly, the image inpainting methods are classified and summarized according to the view of model optimization. Then the common datasets and performance evaluation indicators are introduced, and the performance evaluation and analysis of various deep neural network-based image inpainting algorithms are carried out on the relevant data sets. Finally, the challenges faced by the existing image inpainting methods are analyzed, and the future research works are prospected.
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    Improved YOLOv5 for Small Object Detection Algorithm
    YU Jun, JIA Yinshan
    Computer Engineering and Applications    2023, 59 (12): 201-207.   DOI: 10.3778/j.issn.1002-8331.2302-0157
    Abstract351)      PDF(pc) (566KB)(258)       Save
    Although the current deep learning technology has made amazing progress in the field of large and medium object detection, small object detection is still a challenging problem today due to the limited size of small object and the limitations of convolutional networks. Based on You Only Look Once version 5(hereinafter referred to as YOLOv5) algorithm, this research proposes a YOLO-S model, which is very friendly to small objects. Firstly, on the basis of the orginal output layer with only three layers, a special output layer for small object detection is added by using the cascade network. Secondly, in order to supplement context information and suppress multi-scale feature fusion conflicts, a new supplement context information module CFM and channel and spatial feature thinning module FSM is designed. Finally, the upsampling method is replaced by deconvolution from the original linear interpolation. The dataset uses VisDrone2019, which is specially designed for small objects, to verify the effectiveness of the algorithm. The experimental results show that the mAP@0.5 of YOLO-S is 6.9 percentage points higher than that of YOLOv5.
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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
    Abstract341)      PDF(pc) (645KB)(166)       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.
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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
    Abstract325)      PDF(pc) (5275KB)(200)       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.
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    Review of WebAssembly Application Research for Edge Serverless Computing
    WANG Xin, ZHAO Kai, QIN Bin
    Computer Engineering and Applications    2023, 59 (11): 28-36.   DOI: 10.3778/j.issn.1002-8331.2210-0308
    Abstract301)      PDF(pc) (575KB)(174)       Save
    WebAssembly(Wasm) is a new binary format that is portable, small, fast to load and compatible with the Web. It has the characteristics of high efficiency, security, and openness. The basic concept of edge computing is to run computing tasks on computing resources close to the data source. However, the performance and resources of devices deployed on the edge are usually very limited. In this resource-constrained environment, how to provide low-latency and secure services is an important research direction of edge computing. Serverless is a new way to host applications on infrastructure. At present, it is mainly based on container technology to realize program hosting. Serverless computing is currently the most suitable architecture for edge computing due to its lightweight, function as a service(FaaS), automatic scaling and so on, but it always has problems such as cold start and large memory consumption. Wasm can replace the traditional container and provide an updated, faster, less resource consuming and secure isolation implementation for edge serverless computing. In this paper, the characteristics of edge serverless computing and its application scenarios, as well as the development trend of Wasm are introduced at first. The current research on Wasm-based edge serverless computing is analyzed and the development direction of Wasm runtime alternative containers as the carrier of edge serverless computing is illustrated. Moreover, the problems of edge Wasm serverless computing platform are discussed and the future optimization directions based on deep reinforcement learning and other artificial intelligence algorithms are summarized.
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    Survey of Bioinformatics-Based Protein Function Prediction
    LI Xinhui, QIAN Yurong, YUE Haitao, HU Yue, CHEN Jiaying, LENG Hongyong, MA Mengnan
    Computer Engineering and Applications    2023, 59 (16): 50-62.   DOI: 10.3778/j.issn.1002-8331.2212-0167
    Abstract296)      PDF(pc) (761KB)(171)       Save
    The protein function prediction task aims to provide functional annotations for protein data with missing functional tags. With the development of protein sequencing technology, the number of proteins in the database is growing rapidly, and due to the complexity and multiplicity of protein data, the protein function prediction task is very challenging and has received close attention from researchers. In this paper, the development history of machine learning in protein function prediction is firstly reviewed. Secondly, protein function prediction methods in recent years are categorized and summarized, and the similarities and differences between various algorithms are analyzed. Finally, the problems of protein function prediction are discussed, and future research in this field is anticipated.
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    Review of Application of Deep Learning in Symbolic Music Generation
    CHEN Jishang, Abudukelimu Halidanmu, LIANG Yunze, Abulizi Abudukelimu, Aishan Mikelayi, GUO Wenqiang
    Computer Engineering and Applications    2023, 59 (9): 27-45.   DOI: 10.3778/j.issn.1002-8331.2209-0305
    Abstract271)      PDF(pc) (938KB)(143)       Save
    Symbolic music generation is an important task in the field of music information retrieval. This paper provides a comprehensive summary of deep learning-based symbolic music generation, and existing methods are classified, analyzed, as well as compared. The research status and tasks of symbolic music generation are introduced in detail. It expounds the representation and coding methods of symbolic music, and focuses on the induction, comparison and analysis of deep learning-based models, which are divided into three categories according to different basic structures. It expounds and summarizes the evaluation criteria and datasets in the field of symbolic music generation, and evaluates the performance of representative models. The existing problems in this field are pointed out and corresponding prospects are put forward.
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    Review of Single-Image 3D Face Reconstruction Methods
    WANG Jingting, LI Huibin
    Computer Engineering and Applications    2023, 59 (17): 1-21.   DOI: 10.3778/j.issn.1002-8331.2210-0041
    Abstract271)      PDF(pc) (961KB)(256)       Save
    In recent years, 3D face reconstruction task, as an important part of “digital human” technology, has received great attention from both academia and industry. In particular, 3D face reconstruction task based on a single image has made great progress by fully combining traditional camera model, illumination model, 3D face statistical deformation model with the deep convolutional neural network and deep generative models. This paper focuses on the single-image 3D face reconstruction problem, and divides the existing research works into two categories based on implicit space coding and explicit space regression. The first type of research works optimize the basis coefficient solution and loss function design of the basic 3D face statistical model to improve the reconstruction effect, which has the advantage of robustness in face topology change but lacks detailed features. The second type of research works represent 3D faces in the forms of multiple data in explicit space and regress them directly by deep networks, which can usually obtain more personalized 3D face detail features and have better robustness to interference factors such as illumination and occlusion. Furthermore, based on the commonly used datasets and evaluation metrics, this paper fully explores and compares the advantages and disadvantages of some typical methods of both categories. Finally, it summarizes the whole paper and points out the main challenges and future development trends of the single-image based 3D face reconstruction task.
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    Multimodal False News Detection Based on Fusion Attention Mechanism
    LIU Hualing, CHEN Shanghui, QIAO Liang, LIU Yaxin
    Computer Engineering and Applications    2023, 59 (9): 95-103.   DOI: 10.3778/j.issn.1002-8331.2202-0204
    Abstract267)      PDF(pc) (622KB)(154)       Save
    Exploring efficient modal representation and multimodal information interaction methods has always been a hot topic in the field of multimodal information detection, for which a new fake news detection technology(MAM) is proposed. The MAM method uses a self-attention mechanism combined with position coding and a pre-trained convolutional neural network to extract text and image features respectively. The introduction of a mixed-attention mechanism module for text and image feature interaction, which uses hierarchical feature processing methods to reduce redundant information generated during multimodal interactions. A two-way feature fusion method is used to ensure the integrity of the training information. The multimodal features are weighted and fed into the fully connected network for true and false news classification. The comparative experimental results show that compared with the existing multimodal reference model, the method is almost improved by about 3 percentage points on each classification index, and the visualization experiment finds that the multimodal features obtained by the mixed attention mechanism have stronger generalization ability.
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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
    Abstract262)      PDF(pc) (917KB)(678)       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.
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    Review of Intelligent Decision Optimization of Electric Vehicle Charging Stations Location
    WEI Guanyuan, WANG Guanqun, RUAN Guanmei, GENG Na
    Computer Engineering and Applications    2023, 59 (21): 52-65.   DOI: 10.3778/j.issn.1002-8331.2302-0021
    Abstract255)      PDF(pc) (683KB)(142)       Save
    A reasonable location of electric vehicle(EV) charging stations plays an important role in promoting the development of EV industry and the strategic layout of urban transportation. The relevant literature of intelligent decision optimization of charging station location is systematically reviewed to provide reference for future planning of charging station. The basic principles and influencing factors of EV charging station location are elaborated. Methods of charging demand estimation based on EV trip simulation and data analysis are summarized. The location model of EV charging station based on point demand, origin-destination pair flow demand, and EV trajectory are introduced. The exact algorithm and heuristic algorithm and deep learning algorithm for solving the EV charging station location model are summarized. Finally, limitations of existing studies are discussed, and future research focuses and directions are prospected.
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    Research on Improving YOLOv7’s Small Target Detection Algorithm
    LI Anda, WU Ruiming, LI Xudong
    Computer Engineering and Applications    2024, 60 (1): 122-134.   DOI: 10.3778/j.issn.1002-8331.2307-0004
    Abstract250)      PDF(pc) (884KB)(145)       Save
    With the continuous application of deep learning in domestic object detection, conventional large and medium object detection has made astonishing progress. However, due to the limitations of convolutional networks themselves, there are still issues of missed and false detections in small object detection. Taking dataset Visdrone 2019 and dataset FloW-Img as examples, the YOLOv7 model is studied, and the ELAN module of the backbone network is improved in the network structure. The Focal NeXt block is integrated into the long and short gradient paths of the ELAN module to enhance the feature quality of small targets and improve the contextual information content contained in the output features. The RepLKDeXt module is introduced into the head network, which not only replaces the SPPCSPC module to simplify the overall structure of the model, but also optimizes the ELAN-H structure using multi-channel, large convolutional kernels, and Cat operations. Finally, the SIOU loss function is introduced to replace the CIOU function to improve the robustness of the model. The results show that the improved YOLOv7 model reduces the number of parameters and computational complexity, and its detection performance remains approximately unchanged on the Visdrone 2019 dataset with high small target density. It increases by 9.05 percentage points on the sparse FloW-Img dataset with small targets, further simplifying the model and increasing its applicability.
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    Research on Construction Technology and Development Status of Medical Knowledge Graph
    HUANG Hexuan, WANG Xiaoyan, GU Zhengwei, LIU Jing, ZANG Yanan, SUN Xin
    Computer Engineering and Applications    2023, 59 (13): 33-48.   DOI: 10.3778/j.issn.1002-8331.2209-0475
    Abstract248)      PDF(pc) (732KB)(216)       Save
    As an important branch of artificial intelligence, knowledge graph can realize comprehensive integration of medical concepts and mining potential medical knowledge due to its powerful semantic processing ability and data organization ability, which has become an important means for the development of medical intelligence. Based on this, the latest methods and features of the four processes of medical knowledge graph building:knowledge extraction, knowledge expression, knowledge fusion and knowledge reasoning are discussed, the advantages and disadvantages of different methods are deeply studied and compared, the commonly used datasets in each stage are summarized, the research status of knowledge graph in medical knowledge question and answer, clinical auxiliary diagnosis and treatment, knowledge mining of traditional Chinese medicine and drug research are  reviewed, the application difficulties in each scenario are analyzed. Finally, the limitations and challenges of the existing medical knowledge graph technology are summarized and its future development is prospected.
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    Application Method of Knowledge Graph Construction for UAV Fault Diagnosis
    QIU Ling, ZHANG Ansi, ZHANG Yu, LI Shaobo, LI Chuanjiang, YANG Lei
    Computer Engineering and Applications    2023, 59 (9): 280-288.   DOI: 10.3778/j.issn.1002-8331.2210-0415
    Abstract244)      PDF(pc) (713KB)(170)       Save
    In recent years, the safety and security of UAV operations have faced serious challenges, and it is crucial to ensure safe UAV operations. Based on the emerging research hotspot of knowledge mapping, this paper can make full use of UAV a priori knowledge for fault diagnosis, which can realize component association diagnosis and achieve interpretability of diagnosis results by relying on expert knowledge. At present, there are few studies on knowledge graphs for fault diagnosis, and usually“pre-training”models are used to solve the problem of insufficient data for deep learning model training. Still, the application scenarios of this method are more restricted and cannot provide valuable reference training samples for subsequent researchers. Based on the UAV fault repair manual as the primary data, a remotely supervised data annotation method based on the human school of the aircraft standard is proposed to obtain a substantial and accurately annotated UAV fault corpus, and the rule-based and BiLSTM-CRF network knowledge extraction method is combined according to the data structure characteristics, and the experiments prove that the entity extraction effect is good. Based on the UAV fault diagnosis ontology, this paper completes the construction of the UAV fault diagnosis knowledge graph, stores and visualizes it through Neo4j, and builds an intelligent question and answer system for UAS faults to provide a reasoned and accurate diagnosis for UAV faults, which proves the effectiveness of the knowledge graph in the fault diagnosis field and provides a scientific basis for the construction of the fault diagnosis system based on the knowledge graph.
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    Application Progress of Deep Learning in Epilepsy Detection
    ZHANG Hanming, MA Jingang, ZHANG Ningning, ZHAO Zhenzhen, LI Ming
    Computer Engineering and Applications    2023, 59 (10): 35-47.   DOI: 10.3778/j.issn.1002-8331.2207-0062
    Abstract241)      PDF(pc) (623KB)(124)       Save
    With the increasing number of epileptic patients year by year, it is of great practical significance to detect epileptic diseases timely and accurately. Nowadays, deep learning develops rapidly and is widely used in the medical field. Epilepsy detection task based on deep learning has also become a research hotspot. After combing the relevant literature in recent years, this paper systematically summarizes the application of deep learning algorithm in epilepsy detection. Firstly, the pathogenesis, etiology and treatment of epilepsy are introduced. Secondly, the EEG used in epilepsy detection and the division of the overall process of epilepsy are explained. Then, the differences between traditional machine learning and deep learning in this field are simply compared. This paper focuses on the research progress of detecting EEG signals in various stages of epilepsy by deep learning, including two-stage, three-stage and multi-stage EEG detection, and compares the detection algorithms in various stages of epilepsy. Finally, this paper summarizes and prospects the research status and future development direction in this field.
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    Graph Convolutional Neural Network and Its Application in Image Recognition
    LI Wenjing, BAI Jing, PENG Bin, YANG Zhanyuan
    Computer Engineering and Applications    2023, 59 (22): 15-35.   DOI: 10.3778/j.issn.1002-8331.2302-0273
    Abstract239)      PDF(pc) (803KB)(234)       Save
    Convolutional neural network has found widespread application in the field of image recognition, demonstrating remarkable feature extraction capabilities. However, it is inherently designed for processing structured data in Euclidean space, making it less suitable for handling unstructured data. To address this limitation, graph convolutional neural network leverages spectral and spatial methods to extend the scope of convolutional operations, enabling feature learning in non-Euclidean spaces. GCN possesses translational invariance for graph data, facilitating representation learning for unstructured data. Firstly, the basic principles and improvement work of two types of graph convolutional neural networks based on spectral domain and space domain are explained. Then, around the field of image recognition, the application of graph convolutional neural network in multi-label image recognition, skeleton-based action recognition and hyperspectral image classification is introduced, the research progress is summarized, and the performance comparison and analysis of related models are carried out. Finally, the content of the full text is summarized and the future development direction is looked forward.
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