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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
    Abstract208)      PDF(pc) (917KB)(505)       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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    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
    Abstract1010)      PDF(pc) (787KB)(474)       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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    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
    Abstract634)      PDF(pc) (1009KB)(446)       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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    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
    Abstract795)      PDF(pc) (875KB)(437)       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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    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
    Abstract588)      PDF(pc) (808KB)(385)       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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    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
    Abstract511)      PDF(pc) (929KB)(350)       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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    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
    Abstract653)      PDF(pc) (702KB)(348)       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.
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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
    Abstract318)      PDF(pc) (720KB)(345)       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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    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
    Abstract457)      PDF(pc) (683KB)(312)       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 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
    Abstract450)      PDF(pc) (583KB)(309)       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.
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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
    Abstract412)      PDF(pc) (977KB)(305)       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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    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
    Abstract419)      PDF(pc) (665KB)(294)       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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    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
    Abstract486)      PDF(pc) (869KB)(289)       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.
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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
    Abstract407)      PDF(pc) (691KB)(288)       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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    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
    Abstract622)      PDF(pc) (605KB)(274)       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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    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
    Abstract402)      PDF(pc) (662KB)(267)       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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    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
    Abstract361)      PDF(pc) (815KB)(254)       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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    Review of Real-Time Semantic Segmentation Algorithms for Deep Learning
    HE Jiafeng, CHEN Hongwei, LUO Dehan
    Computer Engineering and Applications    2023, 59 (8): 13-27.   DOI: 10.3778/j.issn.1002-8331.2210-0144
    Abstract339)      PDF(pc) (1161KB)(251)       Save
    Semantic segmentation is a technique to segment different objects in a picture from the perspective of pixels and label each pixel in the original picture. However, due to UAV navigation, remote sensing images, medical diagnosis and other application fields, real-time semantic segmentation is needed. Therefore, the real-time semantic segmentation technology based on deep learning has developed rapidly. There are many technologies and models for real-time semantic segmentation. Based on this, on the basis of studying the related literature, the real-time semantic segmentation technology is introduced by semantic segmentation technology, and the advantages of real-time semantic segmentation are briefly described. Then, the important and difficult points of real-time semantic segmentation are discussed. According to the important and difficult points, the existing related technologies and models are expounded, and the advantages and disadvantages of the technologies and models are summarized. Finally, the challenges faced by real-time semantic segmentation are prospected, and the real-time semantic segmentation is summarized, which provides some theoretical references for the follow-up discussion.
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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
    Abstract349)      PDF(pc) (674KB)(243)       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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    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
    Abstract308)      PDF(pc) (566KB)(237)       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 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
    Abstract318)      PDF(pc) (675KB)(220)       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 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
    Abstract232)      PDF(pc) (961KB)(210)       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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    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
    Abstract186)      PDF(pc) (803KB)(199)       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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    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
    Abstract284)      PDF(pc) (661KB)(199)       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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    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
    Abstract334)      PDF(pc) (801KB)(196)       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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    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
    Abstract299)      PDF(pc) (646KB)(195)       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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    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
    Abstract344)      PDF(pc) (679KB)(193)       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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    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
    Abstract487)      PDF(pc) (830KB)(191)       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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    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
    Abstract378)      PDF(pc) (659KB)(189)       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.
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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
    Abstract239)      PDF(pc) (954KB)(183)       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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    Improved YOLOv5 Lightweight Mask Detection Algorithm
    LIU Chonghao, PAN Lihu, YANG Fan, ZHANG Rui
    Computer Engineering and Applications    2023, 59 (7): 232-241.   DOI: 10.3778/j.issn.1002-8331.2209-0013
    Abstract209)      PDF(pc) (906KB)(179)       Save
    In order to improve the detection efficiency of existing mask detection algorithms, and reduce the parameters and model size, an improved lightweight mask detection algorithm YOLOv5-MBF is proposed. Firstly, the GELU activation function replaces the hard-swish activation function of MobileNetV3 deep network, which optimizes the convergence effect of the model, and the improved MobileNetV3 network replaces the YOLOv5s backbone network, which reduces the calculation amount and improves the speed of model detection. Secondly, the feature pyramid structure of BiFPN is added to fuse with different feature layers, which improves the detection accuracy. At the same time, Mosaic and Mixup data enhancement are used in data processing to improve the generalization and robustness of the model. Focal-Loss EIoU is used as the regression loss function, which optimizes the convergence speed of model training and improves the positioning accuracy of mask and face border. Finally, CBAM attention mechanism is added to make the model pay more attention to important features, suppress insignificant features and improve the detection performance. The experimental results show that the average accuracy of the algorithm is 89.5% on the mask-wearing target and the mask-not-wearing target, the model reasoning speed is increased by 43%, the model parameters are reduced by 49%, and the model size is reduced by 48%, which meets the real-time and detection accuracy requirements of mask detection tasks.
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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
    Abstract212)      PDF(pc) (732KB)(169)       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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    Improved SegFormer Network Based Method for Semantic Segmentation of Remote Sensing Images
    TIAN Xuewei, WANG Jiali, CHEN Ming, DU Shouqing
    Computer Engineering and Applications    2023, 59 (8): 217-226.   DOI: 10.3778/j.issn.1002-8331.2204-0141
    Abstract323)      PDF(pc) (951KB)(163)       Save
    Existing segmentation algorithms have difficulties to accurately segment small objects and object boundaries on remote sensing images, due to the multiple object scales and insufficient semantic information of small objects on remote sensing images. Therefore, an improved SegFormer network semantic segmentation method for remote sensing images is proposed, which combines the features of multiple scales output by the SegFormer encoder in a cascaded manner. When merging high-level semantic information features, the semantic feature fusion module is used to preserve the fuzzy boundaries; when merging detailed information features, the gated attention mechanism module is used to filter some high-level semantic information features to reduce their interference to the detailed information features. After that, the features of multiple scales are up-sampled and connected, and the multi-local channel attention module is used to recalibrate the mapping relationship of the connected features according to the channel context to enhance the final segmentation effect. The experimental results on UAVid and ISPRS Potsdam datasets show that the improved SegFormer segmentation method is better than the current mainstream segmentation methods compared, and has better semantic segmentation effect on small objects and boundaries in remote sensing images.
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    Experimental Research on Image Recognition of Wire Rope Damage Based on Improved YOLOv5
    WANG Hongyao, HAN Shuang, LI Qinyi
    Computer Engineering and Applications    2023, 59 (17): 99-106.   DOI: 10.3778/j.issn.1002-8331.2210-0505
    Abstract129)      PDF(pc) (3673KB)(163)       Save
    Wire rope plays a very important role in coal mine equipment. In order to find the wire rope damage as early as possible, conduct early warning and fault handling in advance, and protect the safety of personnel under the mine, a method of wire rope damage identification and detection based on depth learning is proposed. The target detection algorithm YOLOv5 is adopted and improved. The fast adaptive weighted median filter is used for image pre-processing to improve the recognition accuracy of wire rope damage images. After the improvement, the running speed is increased to 187?ms/piece, and the enhancement effect is good. It integrates CBAM and Transformer prediction heads(TPH) into YOLOv5, and inputs the expanded dataset into the improved model for training and testing. The experimental results show that the improved model has good detection performance, and the final average accuracy rate reaches 0.893, 0.037 higher than the original algorithm, 0.196, 0.162 and 0.102 higher than the traditional detection algorithm SSD, Faster R-CNN and the original YOLOv3, respectively. It shows that the algorithm in this paper has high accuracy and effectively improves the recognition accuracy of wire rope damage images.
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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
    Abstract268)      PDF(pc) (595KB)(160)       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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    Research of Deep Learning-Based Computational Spectral Imaging for Single RGB Image
    JIANG Zhongmin, ZHANG Wanyan, WANG Wenju
    Computer Engineering and Applications    2023, 59 (10): 22-34.   DOI: 10.3778/j.issn.1002-8331.2211-0082
    Abstract213)      PDF(pc) (662KB)(160)       Save
    Deep learning is introduced into computational spectral imaging to address the high cost and long image acquisition time of traditional spectral imaging methods to investigate how spectral information can be recovered from a single RGB image to provide assistance for various computer vision applications. Computational spectral imaging methods for single RGB images based on deep learning lack comprehensive and systematic research. Deep learning algorithms and network models used for computational spectral imaging are summarised, analyzed and compared. The four categories of CNN(convolutional neural networks), GAN(generative adversarial networks), Attention and Transformer are used to sort out supervised learning methods with excellent reconstruction performance in recent years. The unsupervised learning methods are discussed in terms of both self-encoders and domain adaptation. Datasets and evaluation metrics commonly used for the algorithms are listed, and future research trends and development directions are given.
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    Review of Multimodal Sensor Data Fusion in Sentiment Analysis
    JIN Yelei, Gulanbaier Tuerhong, Mairidan Wushouer
    Computer Engineering and Applications    2023, 59 (23): 1-14.   DOI: 10.3778/j.issn.1002-8331.2303-0254
    Abstract159)      PDF(pc) (862KB)(157)       Save
    Sentiment analysis technology in the context of multi-sensor data fusion is a hot research direction in the field of human-computer interaction. With the advancement of deep learning techniques, research on sentiment analysis has shifted from traditional approaches based on single-sensor data to methods that leverage the fusion of multiple sensor data. This paper aims to provide an overview of the research status and challenges of sentiment analysis technology under the framework of multi-sensor data fusion, starting from the definition and development process of multi-sensor data fusion and sentiment analysis technology. It introduces the classic models and traditional methods of multi-sensor data fusion,and discusses the main research directions and achievements in sentiment analysis, including studies related to sentiment analysis based on various data modalities such as speech, visual, textual, and physiological information. Lastly, it presents multimodal sentiment analysis methods based on multi-sensor data fusion, compares the performance of multimodal sentiment analysis with single-modal approaches through experiments, and explores the future prospects and possible research directions of sentiment analysis technology under the framework of multi-sensor data fusion, including cross-lingual sentiment analysis and further applications and advancements of multimodal sentiment analysis technology.
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    Survey of Online Course Recommendation System
    YU Peng, LIU Xingyu, CHENG Hao, YANG Jiaqi, CHEN Guohua, HE Chaobo
    Computer Engineering and Applications    2023, 59 (22): 1-14.   DOI: 10.3778/j.issn.1002-8331.2305-0162
    Abstract184)      PDF(pc) (692KB)(155)       Save
    The rapid development of online education has led to an explosive growth in the number of online courses, and learners are easily caught in inefficient access to course information caused by “course overload”, which has driven the emergence and development of online course recommendation systems. At present, online course recommendation systems have become a hot spot for research, and a large number of methods have been proposed in this area, so it is necessary to systematically review and analyze the latest research progress. This paper first summarizes the basic framework and related concepts of online course recommendation systems, and then focuses on comparing and analyzing various core recommendation methods used in existing online course recommendation systems, including these methods based on association rule mining, matrix factorization, probabilistic model, deep learning, intelligent optimization, semantic computing, and so on. Finally, this paper introduces various evaluation metrics of online course recommendation systems and publicly available datasets, and proposes the future development direction.
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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
    Abstract265)      PDF(pc) (580KB)(154)       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 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
    Abstract209)      PDF(pc) (5275KB)(153)       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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