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Review of Fault Diagnosis Techniques for UAV Flight Control Systems AN Xue, LI Shaobo, ZHANG Yizong, ZHANG Ansi Computer Engineering and Applications    2023, 59 (24): 1-15.   DOI: 10.3778/j.issn.1002-8331.2305-0137 Abstract （348）      PDF（pc） （917KB）（1423）       Knowledge map    Save In recent years, unmanned aerial vehicles（UAVs） have been widely used in various complex fields of military and civilian applications due to their unique advantages such as low operating costs and high mobility. At the same time, the complex and diverse missions have put forward higher requirements for the reliability and safety of UAV systems. The UAV fault diagnosis technology can provide timely and accurate diagnosis results, which helps the maintenance, repair and servicing of UAVs, and is of great significance in enhancing the combat effectiveness of UAVs. Therefore, this paper firstly analyses UAV flight control systems, and classifies the faults. Secondly, the research methods and status quo of UAV fault diagnosis technology are analysed and summarised. Finally, the main challenges faced by UAV fault diagnosis technology are discussed and the future development direction is pointed out; the aim is to provide some reference for researchers in the field of UAV fault diagnosis technology and to promote the improvement of UAV fault diagnosis technology level in China. Reference | Related Articles | Metrics

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Study on Optimization of Cooperative Distribution Path Between UAVs and Vehicles Under Rural E-Commerce Logistics XU Ling, YANG Linchao, ZHU Wenxing, ZHONG Shaojun Computer Engineering and Applications    2024, 60 (1): 310-318.   DOI: 10.3778/j.issn.1002-8331.2306-0115 Abstract （820）      PDF（pc） （666KB）（667）       Knowledge map    Save Drone delivery has emerged as a significant solution to address the challenges of last-mile logistics. The collaborative delivery model between drones and vehicles overcomes the limitations of insufficient drone delivery capacity and enhances safety, making it a vital approach for drone involvement in the delivery process. To tackle the difficulties and high costs associated with “last-mile” delivery in rural e-commerce logistics, this study constructs a mixed-integer programming model. The objective is to minimize delivery costs while considering constraints such as the collaborative drone-vehicle mode and multi drone multi-parcel delivery. A two-stage algorithm is proposed to optimize the paths for drone-vehicle collaborative delivery. In the first stage, a constrained adaptive K-means algorithm is utilized to determine the range of vehicle docking points. In the second stage, an improved genetic algorithm that incorporates hill climbing and splitting operators is employed to identify the optimal delivery paths for drones and vehicles. Subsequently, a case study experiment is conducted to validate the feasibility and effectiveness of the model and algorithm. The research findings are expected to offer novel insights and valuable references for cost reduction and efficiency improvement in last-mile delivery for rural e-commerce logistics. Reference | Related Articles | Metrics

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Review on Research and Application of Deep Learning-Based Target Detection Algorithms ZHANG Yangting, HUANG Deqi, WANG Dongwei, HE Jiajia Computer Engineering and Applications    2023, 59 (18): 1-13.   DOI: 10.3778/j.issn.1002-8331.2305-0310 Abstract （1061）      PDF（pc） （662KB）（648）       Knowledge map    Save With the continuous development of deep learning, deep convolutional neural networks are increasingly used in the field of target detection and are now applied in many fields such as agriculture, transportation, and medicine. Compared with traditional feature-based manual methods, deep learning-based target detection methods can learn both low-level and high-level image features with better detection accuracy and generalization ability. To outline and summarize the latest advances and technologies in the field of target detection, the status of deep learning-based target detection algorithms and applications is reviewed by analyzing the deep learning-based target detection technologies in recent years. Firstly, the development, advantages and disadvantages of two kinds of target detection network architectures, two phases and single phase, are summarized; secondly, the backbone network, data set and evaluation metrics are described, the detection accuracy of classical algorithms are compared, and the improvement strategies of classical target detection algorithms are summarized; finally, the current stage of target detection applications are discussed, and future research priorities in the field of target detection are proposed. Reference | Related Articles | Metrics

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Review of SLAM Based on Lidar LIU Mingzhe, XU Guanghui, TANG Tang, QIAN Xiaojian, GENG Ming Computer Engineering and Applications    2024, 60 (1): 1-14.   DOI: 10.3778/j.issn.1002-8331.2308-0455 Abstract （840）      PDF（pc） （854KB）（551）       Knowledge map    Save Simultaneous localization and mapping (SLAM) is a crucial technology for autonomous mobile robots and autonomous driving systems, with a laser scanner (also known as lidar) playing a vital role as a supporting sensor for SLAM algorithms. This article provides a comprehensive review of lidar-based SLAM algorithms. Firstly, it introduces the overall framework of lidar-based SLAM, providing detailed explanations of the functions of the front-end odometry, back-end optimization, loop closure detection, and map building modules, along with a summary of the algorithms used. Secondly, it presents descriptions and summaries of representative open-source algorithms in a sequential order of 2D to 3D and single-sensor to multi-sensor fusion. Additionally, it discusses commonly used open-source datasets, precision evaluation metrics, and evaluation tools. Lastly, it offers an outlook on the development trends of lidar-based SLAM technology from four dimensions: deep learning, multi-sensor fusion, multi-robot collaboration, and robustness research. Reference | Related Articles | Metrics

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Research on Urban Logistics Distribution Mode of Bus-Assisted Drones PENG Yong, REN Zhi Computer Engineering and Applications    2024, 60 (7): 335-343.   DOI: 10.3778/j.issn.1002-8331.2212-0252 Abstract （635）      PDF（pc） （755KB）（527）       Knowledge map    Save The rapid development of e-commerce forces the continuous transformation and upgrading of the logistics industry. In view of the fact that local governments encourage the development of public transport and advocate green and low-carbon logistics distribution mode, a distribution mode of bus-assisted drone is studied. After explaining the problem, a mathematical model with the lowest distribution cost is constructed, and a heuristic algorithm of smart general variable neighborhood search metaheuristic is designed to solve the problem. At the same time, in order to improve the efficiency of the algorithm, K-means clustering and greedy algorithm are introduced to generate the initial solution. Firstly, aiming at different scale examples, a variety of local search strategies and a variety of algorithms are compared to verify the effectiveness of the algorithm. Secondly, by selecting the standard CVRP as example, the single truck distribution mode and truck-drone collaborative distribution mode are compared with the distribution mode of bus-assisted drone to prove its cost and time advantages. Finally, Beijing Bus Rapid Transit Line 2 and its surrounding customer points are selected, and sensitivity analysis is made by changing the bus stop spacing and departure interval, result shows that the impact of increasing the stop spacing is greater than the change of departure interval. Reference | Related Articles | Metrics

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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 Abstract （426）      PDF（pc） （720KB）（438）       Knowledge map    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. Reference | Related Articles | Metrics

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Survey of Sentiment Analysis Algorithms Based on Multimodal Fusion GUO Xu, Mairidan Wushouer, Gulanbaier Tuerhong Computer Engineering and Applications    2024, 60 (2): 1-18.   DOI: 10.3778/j.issn.1002-8331.2305-0439 Abstract （592）      PDF（pc） （954KB）（433）       Knowledge map    Save Sentiment analysis is an emerging technology that aims to explore people’s attitudes toward entities and can be applied to various domains and scenarios, such as product evaluation analysis, public opinion analysis, mental health analysis and risk assessment. Traditional sentiment analysis models focus on text content, yet some special forms of expression, such as sarcasm and hyperbole, are difficult to detect through text. As technology continues to advance, people can now express their opinions and feelings through multiple channels such as audio, images and videos, so sentiment analysis is shifting to multimodality, which brings new opportunities for sentiment analysis. Multimodal sentiment analysis contains rich visual and auditory information in addition to textual information, and the implied sentiment polarity (positive, neutral, negative) can be inferred more accurately using fusion analysis. The main challenge of multimodal sentiment analysis is the integration of cross-modal sentiment information; therefore, this paper focuses on the framework and characteristics of different fusion methods and describes the popular fusion algorithms in recent years, and discusses the current multimodal sentiment analysis in small sample scenarios, in addition to the current development status, common datasets, feature extraction algorithms, application areas and challenges. It is expected that this review will help researchers understand the current state of research in the field of multimodal sentiment analysis and be inspired to develop more effective models. Reference | Related Articles | Metrics

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Survey on Credit Card Transaction Fraud Detection Based on Machine Learning JIANG Hongxun, JIANG Junyi, LIANG Xun Computer Engineering and Applications    2023, 59 (21): 1-25.   DOI: 10.3778/j.issn.1002-8331.2302-0129 Abstract （589）      PDF（pc） （674KB）（370）       Knowledge map    Save Machine learning has its distinctiveness in credit card transaction detection and faces a more complex environment. Since the intervention of human intelligence, machine learning encounters harder challenges in fraud detection than the ones of face recognition and driverlessness, which leads to failures if only applying the processes of engineering disciplines. This paper depicts the 2000-since research history of credit card anti-fraud; identifies the definition, scope, technical streams, applications, and other key concepts, and their interconnections in the field of detection oriented machine learning; analyzes the general architecture of fraud detection and summarizes the state-of-the-art of transaction fraud detection research in terms of feature engineering, models/algorithms, and evaluation metrics; discusses various detection algorithms of credit card transaction fraud and enumerates their original intention, core ideas, solution methods, advantages or disadvantages, and relevant extensions; highlights unsupervised, supervised, and semi-supervised learning models of fraud recognition, as well as various ensembles such as models cascading and aggregation; addresses three major challenges, i.e., massive data, sample skew, and concept drift, and compiles the latest progresses to alleviate these problems. This paper concludes with the limitations, controversies, and challenges of machine learning on credit card fraud recognition, and provides the trend analysis and suggestions for future research directions. Reference | Related Articles | Metrics

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Review on Human Action Recognition Methods Based on Multimodal Data WANG Cailing, YAN Jingjing, ZHANG Zhidong Computer Engineering and Applications    2024, 60 (9): 1-18.   DOI: 10.3778/j.issn.1002-8331.2310-0090 Abstract （217）      PDF（pc） （8541KB）（356）       Knowledge map    Save Human action recognition (HAR) is widely applied in the fields of intelligent security, autonomous driving and human-computer interaction. With advances in capture equipment and sensor technology, the data that can be acquired for HAR is no longer limited to RGB data, but also multimodal data such as depth, skeleton, and infrared data. Feature extraction methods in HAR based on RGB and skeleton data modalities are introduced in detail, including handcrafted-based and deep learning-based methods. For RGB data modalities, feature extraction algorithms based on two-stream convolutional neural network (2s-CNN), 3D convolutional neural network (3DCNN) and hybrid network are analyzed. For skeleton data modalities, some popular pose estimation algorithms for single and multi-person are firstly introduced. The classification algorithms based on convolutional neural network (CNN), recurrent neural network (RNN), and graph convolutional neural network (GCN) are analyzed stressfully. A further comprehensive demonstration of the common datasets for both data modalities is presented. In addition, the current challenges are explored based on the corresponding data structure features of RGB and skeleton. Finally, future research directions for deep learning-based HAR methods are discussed. Reference | Related Articles | Metrics

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Improved YOLOv8s Model for Small Object Detection from Perspective of Drones PAN Wei, WEI Chao, QIAN Chunyu, YANG Zhe Computer Engineering and Applications    2024, 60 (9): 142-150.   DOI: 10.3778/j.issn.1002-8331.2312-0043 Abstract （233）      PDF（pc） （5858KB）（334）       Knowledge map    Save Facing with the problems of small and densely distributed image targets, uneven class distribution, and model size limitation of hardware conditions, object detection from the perspective of drones has less precise results. A new improved model based on YOLOv8s with multiple attention mechanisms is proposed. To solve the problem of shared attention weight parameters in receptive field features and enhance feature extraction ability, receptive field attention convolution and CBAM (concentration based attention module) attention mechanism are introduced into the backbone, adding attention weight in channel and spatial dimensions. By introducing large separable kernel attention into feature pyramid pooling layers, information fusion between different levels of features is increased. The feature layers with rich semantic information of small targets are added to improve the neck structure. The inner-IoU loss function is used to improve the MPDIoU (minimum point distance based IoU) function and the inner-MPDIoU instead of the original loss function is used to enhance the learning ability for difficult samples. The experimental results show that the improved YOLOv8s model has improved mAP, P, and R by 16.1%, 9.3%, and 14.9% respectively on the VisDrone dataset, surpassing YOLOv8m in performance and can be effectively applied to unmanned aerial vehicle visual detection tasks. Reference | Related Articles | Metrics

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Review of Path Planning Algorithms for Robot Navigation CUI Wei, ZHU Fazheng Computer Engineering and Applications    2023, 59 (19): 10-20.   DOI: 10.3778/j.issn.1002-8331.2301-0088 Abstract （595）      PDF（pc） （595KB）（319）       Knowledge map    Save Path planning is one of the key technologies for robot navigation. An excellent path planning algorithm can quickly find the best collision-free path and improve operational efficiency. Most existing classification methods have difficulty in expressing the differences and connections between algorithms. To distinguish different path planning algorithms more clearly, they are divided into graph-based search, bionic-based, potential field-based, velocity space-based and sampling-based algorithms based on their principle and nature. This paper introduces the concept, characteristics, and development status of each type of algorithm, analyzes the more widely used sample-based algorithms from the perspective of single-query and multi-query algorithms, and the advantages and problems of different types of path planning algorithms are compared and summarized. Finally, the future development trend of robot path planning algorithms in terms of multi-robot collaboration, multi-algorithm fusion and adaptive planning is prospected. Reference | Related Articles | Metrics

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Review of Deep Learning Methods for MRI Reconstruction DENG Gewen, WEI Guohui, MA Zhiqing Computer Engineering and Applications    2023, 59 (20): 67-76.   DOI: 10.3778/j.issn.1002-8331.2302-0057 Abstract （557）      PDF（pc） （580KB）（301）       Knowledge map    Save Magnetic resonance imaging（MRI） is a commonly used imaging technique in the clinic, but the excessive imaging time limits its further development. Image reconstruction from undersampled k-space data has been an important part of accelerating MRI imaging. In recent years, deep learning has shown great potential in MRI reconstruction, and its reconstruction results and efficiency are better than traditional compressed sensing methods. To sort out and summarize the current deep learning-based MRI reconstruction methods, it firstly introduces the definition of MRI reconstruction problem, secondly analyzes the application of deep learning in data-driven end-to-end reconstruction and model-driven unrolled optimization reconstruction, then provides evaluation metrics and common datasets for reconstruction, and finally discusses the challenges faced by current MRI reconstruction and future research directions. Reference | Related Articles | Metrics

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Review of 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 Abstract （302）      PDF（pc） （961KB）（297）       Knowledge map    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. Reference | Related Articles | Metrics

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Survey of Few-Shot Image Classification Based on Deep Meta-Learning ZHOU Bojun, CHEN Zhiyu Computer Engineering and Applications    2024, 60 (8): 1-15.   DOI: 10.3778/j.issn.1002-8331.2308-0271 Abstract （228）      PDF（pc） （1091KB）（293）       Knowledge map    Save Deep meta-learning has emerged as a popular paradigm for addressing few-shot classification problems. A comprehensive review of recent advancements in few-shot image classification algorithms based on deep meta-learning is provided. Starting from the problem description, the categorizes of the algorithms based on deep meta-learning for few-shot image classification are summarized, and commonly used few-shot image classification datasets and evaluation criteria are introduced. Subsequently, typical models and the latest research progress are elaborated in three aspects: model-based deep meta-learning methods, optimization-based deep meta-learning methods, and metric-based deep meta-learning methods. Finally, the performance analysis of existing algorithms on popular public datasets is presented, the research hotspots in this topic are summarized, and its future research directions are discussed. Reference | Related Articles | Metrics

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Algorithm for Real-Time Vehicle Detection from UAVs Based on Optimizing and Improving YOLOv8 SHI Tao, CUI Jie, LI Song Computer Engineering and Applications    2024, 60 (9): 79-89.   DOI: 10.3778/j.issn.1002-8331.2312-0291 Abstract （215）      PDF（pc） （4614KB）（293）       Knowledge map    Save To address the problems of low accuracy, easy interference from background environment and difficulty in detecting small target vehicles of existing UAV vehicle detection algorithms, an improved UAV vehicle detection algorithm YOLOv8-CX is proposed based on YOLOv8. By integrating the advantages of Deformable Convolutional Networks v1-3, a C2f-DCN module is proposed to flexibly sample features and better extract features between vehicles of different sizes. Utilizing the idea of large separable kernel attention, a SPPF-LSKA module is proposed with long-range dependency and self-adaptability, which can effectively reduce background interference on vehicle detection. In the neck network, a CF-FPN (ment network for tiny object deteciton) feature fusion structure is adopted to enhance the detection accuracy of small targets by combining contextual information and suppressing conflicts between features at different scales. Finally, the original YOLOv8 head is replaced with a Dynamic Head detection head. By unifying scale, space and task, the three types of attention mechanisms, the model detection performance is further improved. Experimental results show that on the Mapsai dataset, compared with the original algorithm, the improved algorithm increases the accuracy (P), recall (R) and mean average precision (mAP) by 8.5, 11.2 and 6.2 percentage points respectively, and the algorithm detection speed reaches 72.6 FPS, meeting the real-time requirements of UAV vehicle detection. By comparing with other mainstream target detection algorithms, the effectiveness and superiority of this method are validated. Reference | Related Articles | Metrics

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Survey of Research Methods for Low Light Image Enhancement PENG Daxin, ZHEN Tong, LI Zhihui Computer Engineering and Applications    2023, 59 (18): 14-27.   DOI: 10.3778/j.issn.1002-8331.2210-0143 Abstract （509）      PDF（pc） （645KB）（286）       Knowledge map    Save The purpose of low-light image enhancement is to restore images with complete details in low-light conditions, and it has gradually become a hot spot in computer image processing research. The quality of image imaging is crucial to intelligent security, video surveillance, and other scenarios and has a very broad application prospect in related industries. In order to study low-light image enhancement in depth, firstly, the traditional low-light image enhancement methods are classified and analyzed in detail, and then the image enhancement methods based on deep learning are listed, and the various networks used and the problems solved are detailed and compared the mentioned methods in detail. Then, the data set is analyzed and studied in detail, and some commonly used evaluation indicators are briefly sorted out. Finally, it summarizes the content, points out the difficulties in the current research, and points out the research goals for the future. Reference | Related Articles | Metrics

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Review of Application of Machine Learning in Radiomics Analysis LU Huimin, XUE Han, WANG Yilong, WANG Guizeng, SANG Pengcheng Computer Engineering and Applications    2023, 59 (17): 22-34.   DOI: 10.3778/j.issn.1002-8331.2210-0435 Abstract （503）      PDF（pc） （5275KB）（283）       Knowledge map    Save Radiomics is a technique for quantitatively extracting features from standard medical images. The construction of predictive or diagnostic models with the assistance of machine learning enables data to be extracted and applied in clinical decision support systems to improve diagnostic accuracy, which has been widely used in tumor staging, cancer detection, survival analysis and other tasks. The application and research progress of machine learning in radiomics analysis are reviewed. The applicability and limitations of machine learning algorithms in each stage of radiomics analysis are emphatically discussed, and the representative algorithms are thoroughly sorted out and analyzed in terms of principles and application effects. The evaluation methods to the work of each stage in the radiomics analysis are comprehensively introduced. The publicly available medical image datasets and software toolkits for radiomics feature extraction are organized. Finally, the future development of machine learning in radiomics is discussed. Reference | Related Articles | Metrics

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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 Abstract （295）      PDF（pc） （803KB）（279）       Knowledge map    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. Reference | Related Articles | Metrics

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Review of Development of Deep Learning Optimizer CHANG Xilong, LIANG Kun, LI Wentao Computer Engineering and Applications    2024, 60 (7): 1-12.   DOI: 10.3778/j.issn.1002-8331.2307-0370 Abstract （236）      PDF（pc） （1327KB）（273）       Knowledge map    Save Optimization algorithms are the most critical  factor in improving the performance of deep learning models, achieved by minimizing the loss function. Large language models (LLMs), such as GPT, have become the research focus in the field of natural language processing, the optimization effect of traditional gradient descent algorithm has been limited. Therefore, adaptive moment estimation algorithms have emerged, which are significantly superior to traditional optimization algorithms in generalization ability. Based on gradient descent, adaptive gradient, and adaptive moment estimation algorithms, and the pros  and cons of optimization algorithms are analyzed. This paper applies optimization algorithms to the Transformer architecture and selects the French-English translation task as the evaluation benchmark. Experiments have shown that adaptive moment estimation algorithms can effectively improve the performance of the model in machine translation tasks. Meanwhile, it discusses the development direction and applications of optimization algorithms. Reference | Related Articles | Metrics

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Survey on Video-Text Cross-Modal Retrieval CHEN Lei, XI Yimeng, LIU Libo Computer Engineering and Applications    2024, 60 (4): 1-20.   DOI: 10.3778/j.issn.1002-8331.2306-0382 Abstract （280）      PDF（pc） （3662KB）（267）       Knowledge map    Save Modalities define the specific forms in which data exist. The swift expansion of various modal data types has brought multimodal learning into the limelight. As a crucial subset of this field, cross-modal retrieval has achieved noteworthy advancements, particularly in integrating images and text. However, videos, as opposed to images, encapsulate a richer array of modal data and offer a more extensive spectrum of information. This richness aligns well with the growing user demand for comprehensive and adaptable information retrieval solutions. Consequently, video-text cross-modal retrieval has emerged as a burgeoning area of research in recent times. To thoroughly comprehend video-text cross-modal retrieval and its state-of-the-art developments, a methodical review and summarization of the existing representative methods is conducted. Initially, the focus is on analyzing current deep learning-based unidirectional and bidirectional video-text cross-modal retrieval methods. This analysis includes an in-depth exploration of seminal works within each category, highlighting their strengths and weaknesses. Subsequently, the discussion shifts to an experimental viewpoint, introducing benchmark datasets and evaluation metrics specific to video-text cross-modal retrieval. The performance of several standard methods in benchmark datasets is compared. Finally, the application prospects and future research challenges of video- text cross-modal retrieval are discussed. Reference | Related Articles | Metrics

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Improved Road Damage Detection Algorithm of YOLOv8 LI Song, SHI Tao, JING Fangke Computer Engineering and Applications    2023, 59 (23): 165-174.   DOI: 10.3778/j.issn.1002-8331.2306-0205 Abstract （323）      PDF（pc） （671KB）（265）       Knowledge map    Save Road damage detection is an important task to ensure road safety and realize timely repair of road damage. Aiming at the problems of low detection efficiency, high cost and difficulty in applying to mobile terminal devices in existing Road Damage detection algorithms, a lightweight road damage detection algorithm YOLOV8-Road Damage（YOLOV8-RD） with improved YOLOv8 is proposed. First, combining the advantages of CNN and Transformer, a BOT module that can extract global and local feature information of road damage images is proposed to adapt to the large-span and elongated features of crack objects. Then, coordinate attention（CA） is introduced in the end of backbone network and neck network to embed the location information into the channel attention, strengthen the feature extraction ability, and suppress the interference of irrelevant features. In addition, C2fGhost module is used in YOLOv8 neck network to reduce floating point computation in feature channel fusion process, reduce the number of model parameters, and improve feature expression performance. The experimental results show that in RDD2022 data set and Road Damage data set, the improved algorithm is 2% and 3.7% higher than the original algorithm compared with mAP50, while the number of model parameters is only 2.8×106 and the computation amount is only 7.3×109, which are reduced by 6.7% and 8.5% respectively. The detection speed of the algorithm reaches 88 FPS, which can accurately detect the road damage target in real time. Compared with other mainstream target detection algorithms, the effectiveness and superiority of this method are verified. Reference | Related Articles | Metrics

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Survey on Attack Methods and Defense Mechanisms in Federated Learning ZHANG Shiwen, CHEN Shuang, LIANG Wei, LI Renfa Computer Engineering and Applications    2024, 60 (5): 1-16.   DOI: 10.3778/j.issn.1002-8331.2306-0243 Abstract （228）      PDF（pc） （792KB）（256）       Knowledge map    Save The attack and defense techniques of federated learning are the core issue of federated learning system security. The attack and defense techniques of federated learning can significantly reduce the risk of being attacked and greatly enhance the security of federated learning systems. Deeply understanding the attack and defense techniques of federated learning can advance research in the field and achieve its widespread application of federated learning. Therefore, it is of great significance to study the attack and defense techniques of federated learning. Firstly, this paper briefly introduces the concept, basic workflow, types, and potential existing security issues of federated learning. Subsequently, the paper introduces the attacks that the federated learning system may encounter, and relevant research is summarized during the introduction. Then, starting from whether the federated learning system has targeted defense measures, the defense measures are divided into two categories：universal defense measures and targeted defense measures, and targeted summary are made. Finally, it reviews and analyzes the future research directions for the security of federated learning, providing reference for relevant researchers in their research work on the security of federated learning. Reference | Related Articles | Metrics

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Small Sample Steel Plate Defect Detection Algorithm of Lightweight YOLOv8 DOU Zhi, GAO Haoran, LIU Guoqi, CHANG Baofang Computer Engineering and Applications    2024, 60 (9): 90-100.   DOI: 10.3778/j.issn.1002-8331.2311-0070 Abstract （179）      PDF（pc） （5010KB）（248）       Knowledge map    Save The surface area of steel plate is large, and the surface defects are very common, and showing the characteristics of multi-class and small amount. Deep learning is difficult to be effectively applied to the detection of such small sample defects. In order to solve this problem, a small sample steel plate defect detection algorithm based on lightweight YOLOv8 is proposed. Firstly, an interactive data augmentation algorithm based on fuzzy search is proposed, which can effectively solve the problem that the network model cannot be effectively trained due to the lack of training samples, making it possible for deep learning to be applied in this field. Then, the LMRNet (lightweight multi-scale residual networks) network is designed to replace the backbone of YOLOv8, to achieve the lightweight of the network model and improve its portability. Finally, the CBFPN (context bidirectional feature pyramid network) and ECSA (efficient channel spatial attention) modules are proposed to make the network more effective in extracting and fusing scar features, and the Wise-IoU loss function is adopted to improve the detection performance. The comparative experimental results show that compared with the original YOLOv8 algorithm, the amount of parameters of the improved network is only 30% of the original network, the amount of calculation is 49% of the original network, the FPS is increased by 9 frame/s. The accuracy rate, recall rate and mAP have increased by 2.9, 6.5 and 5.5 percentage points respectively. Experimental results fully verify the advantages of the proposed algorithm. Reference | Related Articles | Metrics

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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 Abstract （424）      PDF（pc） （761KB）（242）       Knowledge map    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. Reference | Related Articles | Metrics

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Research Review on Deep Reinforcement Learning for Solving End-to-End Navigation Problems of Mobile Robots HE Li, YAO Jiacheng, LIAO Yuxin, ZHANG Wenzhi, LU Zhaoqing, YUAN Liang, XIAO Wendong Computer Engineering and Applications    2024, 60 (14): 1-13.   DOI: 10.3778/j.issn.1002-8331.2312-0256 Abstract （111）      PDF（pc） （4646KB）（241）       Knowledge map    Save Autonomous navigation is the prerequisite and foundation for mobile robots to accomplish complex tasks. Traditional autonomous navigation systems rely on the accuracy of maps and cannot adapt to highly complex industrial and service scenarios. End-to-end navigation methods for mobile robots that do not rely on a priori map information and are able to make autonomous decisions through deep reinforcement learning, and environment interaction learning have become a new research hotspot. Most existing classifications cannot comprehensively summarize the challenges and opportunities of end-to-end navigation problems. Based on the characteristics of end-to-end navigation systems, the challenges of the navigation problem are attributed to the key issues of poor perception ability of navigation agents, ineffective learning and poor generalization ability of navigation strategies. The research status and development trends of end-to-end navigation systems are described. Representative research results in recent years addressing these key issues are detailed respectively, and their advantages and shortcomings are summarized. Finally, the future development trends of end-to-end navigation for mobile robots are prospectively envisioned in aspects such as visual language navigation, multi-agents collaborative navigation, end-to-end navigation for fusion super-resolution reconstructed images and interpretable end-to-end navigation, providing certain insights for the research and application of end-to-end navigation for mobile robots. Reference | Related Articles | Metrics

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Process of Weakly Supervised Salient Object Detection YU Junwei, GUO Yuansen, ZHANG Zihao, MU Yashuang Computer Engineering and Applications    2024, 60 (10): 1-15.   DOI: 10.3778/j.issn.1002-8331.2308-0206 Abstract （164）      PDF（pc） （6029KB）（237）       Knowledge map    Save Salient object detection aims to accurately detect and locate the most attention-grabbing objects or regions in images or videos, facilitating better object recognition and scene analysis. Despite the effectiveness of fully supervised saliency detection methods, acquiring large pixel-level annotated datasets is challenging and costly. Weakly supervised detection methods utilize relatively easy-to-obtain image-level labels or noisy weak labels to train models, demonstrating good performance in practical applications. This paper comprehensively compares the mainstream methods and application scenarios of fully supervised and weakly supervised saliency detection methods, and then analyzes the data annotation methods using weak labels and their impact on salient object detection. The latest research progress in salient object detection under weakly supervised conditions is reviewed, and the performance of various weakly supervised methods is compared on several public datasets. Finally, the potential applications of weakly supervised saliency detection methods in special fields such as agriculture, medicine and military are discussed, highlighting the existing challenges and future development trends in this research area. Reference | Related Articles | Metrics

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Improved YOLOv8 Multi-Scale and Lightweight Vehicle Object Detection Algorithm ZHANG Lifeng, TIAN Ying Computer Engineering and Applications    2024, 60 (3): 129-137.   DOI: 10.3778/j.issn.1002-8331.2309-0145 Abstract （236）      PDF（pc） （713KB）（233）       Knowledge map    Save To address issues such as high hardware requirements, low detection accuracy, and a high rate of missed overlapping targets in traditional vehicle object detection models, a modified vehicle object detection algorithm called RBT-YOLO based on YOLOv8 is proposed. The main network is reconstructed using a multi-scale fusion approach. BiFPN is improved by adding convolutional operations and adjusting input/output channel numbers to adapt to YOLOv8, enhancing its feature fusion capability. After the feature maps are output from the Neck section, a lightweight attention mechanism called Triplet Attention is introduced to enhance the feature extraction ability of the model. To address the issue of high target overlap in real scenarios, SoftNMS (soft non-maximum suppression) is used to replace the original NMS, making the model to handle the candidate boxes more gentle, thereby strengthening detection capabilities of the model and improving recall rates. Experimental results on the Pascal VOC and MS COCO datasets demonstrate that the proposed RBT-YOLO outperforms the original model, reducing parameters and computations by approximately 60%, the mAP improved by 2.6 and 3.0 percentage points, and excelling in both size and precision compared to other classic detection models, thus demonstrating strong practical utility. Reference | Related Articles | Metrics

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Review of Connected Autonomous Vehicle Cooperative Control at On-Ramp Merging Areas LI Chun, WU Zhizhou, ZENG Guang, ZHAO Xin, YANG Zhidan Computer Engineering and Applications    2024, 60 (12): 1-17.   DOI: 10.3778/j.issn.1002-8331.2310-0310 Abstract （127）      PDF（pc） （5963KB）（225）       Knowledge map    Save The area where vehicles conduct interchanges is designated as the on-ramp merging area. The traffic efficiency in the ramp merging area drastically decreases if the mainline and ramp traffic flow density reaches saturation. As a current research hotspot in transportation, intelligent network technology, relying on the high-precision motion control and high-efficiency communication of connected-automated vehicle (CAV), can significantly improve the traffic efficiency in the merging area. The fusion strategies used by CAV are assessed in this research utilizing three different control paradigms: feedback control, optimal control, and reinforcement learning. The shortcomings of the three methods in this scenario are summarized, and specific improvement measures are given by reviewing existing research. Also, it offers a thorough summary of the most recent developments and trends in this particular scientific field. Reference | Related Articles | Metrics

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Vehicle Detection Algorithm Based on Improved YOLOv8 in Traffic Surveillance ZHOU Fei, GUO Dudu, WANG Yang, WANG Qingqing, QIN Yin, YANG Zhuomin, HE Haijun Computer Engineering and Applications    2024, 60 (6): 110-120.   DOI: 10.3778/j.issn.1002-8331.2310-0101 Abstract （201）      PDF（pc） （817KB）（220）       Knowledge map    Save To address the current problems of insufficient vehicle detection accuracy and slow detection speed in complex traffic monitoring scenarios, a lightweight vehicle detection algorithm based on YOLOv8 model is proposed. Firstly, FasterNet is used to replace the backbone feature extraction network of YOLOv8, which reduces redundant computation and memory access, and improves the detection accuracy and inference speed of the model.Secondly, the SimAM attention module is added to the Backbone and Neck sections, which enhances the important features of the target vehicles without increasing the original network parameters, and improves the feature fusion capability. Then, to address the problem of poor detection of small-sized vehicles under dense traffic flow, a small target detection head is added to better capture the features and contextual information of small-sized vehicles. Finally, Wise-IoU, which can adaptively adjust the weight coefficients, is used as the loss function of the improved model, which enhances the regression performance of the bounding box and the robustness of the detection.The experimental results on the UA-DETRAC dataset show that compared with the original model, the improved method in this paper is able to achieve better detection accuracy and speed in the traffic monitoring system, with the mAP and FPS improved by 3.06 percengtage points and 3.36%, respectively, which effectively improves the problem of the poor detection of small-target vehicles in the complex traffic scenarios, and achieves a good balance between accuracy and speed. Reference | Related Articles | Metrics

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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 Abstract （388）      PDF（pc） （884KB）（218）       Knowledge map    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. Reference | Related Articles | Metrics

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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 Abstract （342）      PDF（pc） （683KB）（213）       Knowledge map    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. Reference | Related Articles | Metrics

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Small Object Detection Algorithm Based on ATO-YOLO SU Jia, QIN Yichang, JIA Ze, WANG Jing Computer Engineering and Applications    2024, 60 (6): 68-77.   DOI: 10.3778/j.issn.1002-8331.2308-0385 Abstract （206）      PDF（pc） （795KB）（210）       Knowledge map    Save Small object detection is of great significance in the field of computer vision. However, existing methods often suffer from issues such as missed detection and false alarms when dealing with challenges like scale variation, dense object arrangement, and irregular layouts. To address these problems, ATO-YOLO, an improved version of the YOLOv5 algorithm is proposed. Firstly, this paper introduces an adaptive feature extraction (AFE) module that incorporates an attention mechanism to enhance the feature representation capability of the detection model. By dynamically adjusting the weight allocation to highlight key object features, AFE improves the accuracy and robustness of object detection tasks in various scenarios. Secondly, a triple feature fusion (TFF) mechanism is designed to effectively utilize multi-scale information by fusing feature maps from different scales, resulting in more comprehensive object features and enhanced detection performance for small objects. Lastly, an output reconstruction (ORS) module is introduced, which removes the large object detection layer and adds a small object detection layer, enabling precise localization and recognition of small objects. This module also reduces model complexity and improves detection speed compared to the original model. Experimental results demonstrate that the ATO-YOLO algorithm achieves an mAP@0.5 of 38.2% on the VisDrone dataset, a 6.1?percentage points improvement over YOLOv5, with a relative FPS increase of 4.4%. This algorithm enables fast and accurate detection of small objects. Reference | Related Articles | Metrics

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Improved YOLOv8 Small Target Detection Algorithm in Aerial Images FU Jinyi, ZHANG Zijia, SUN Wei, ZOU Kaixin Computer Engineering and Applications    2024, 60 (6): 100-109.   DOI: 10.3778/j.issn.1002-8331.2311-0281 Abstract （225）      PDF（pc） （771KB）（209）       Knowledge map    Save In aerial image detection task, object and the overall image size are small, scales have different characteristics and detail information is not clear, it can cause leak and mistakenly identified problems, an improved small target detection algorithm CA-YOLOv8 is proposed. Channel feature partial convolution (CFPConv) is designed. Based on this, it reconstructs a Bottleneck structure in C2f, which is named CFP_C2f. In this way, some C2f modules in YOLOv8 head and neck are replaced, the effective channel feature weights are enhanced, and the ability to obtain multi-scale detail features is improved. A context aggregated module (CAM) is embedded to improve the context aggregation ability, optimize the response of feature channels, and strengthen the ability to perceive the details of deep features. The NWD loss function is added and combined with CIoU as a positioning regression loss function to reduce the sensitivity of position bias. By making full use of the advantages of multiple attention mechanism, the original detection head is replaced with DyHead (dynamic head). In the experiment of VisDrone2019 dataset, the improved algorithm reduces the number of parameters by 33.3% compared with the original YOLOv8s model, and the detection accuracy of mAP50 and mAP50：95 increases by 8.7 and 5.7 percentage points respectively, showing good performance and confirming its effectiveness. Reference | Related Articles | Metrics

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Review of Application of Deep Learning in Colon Polyp Segmentation SUN Fuyan, WANG Qiong, LYU Zongwang, GONG Chunyan Computer Engineering and Applications    2023, 59 (23): 15-27.   DOI: 10.3778/j.issn.1002-8331.2303-0124 Abstract （296）      PDF（pc） （626KB）（209）       Knowledge map    Save Most colorectal cancers originate from malignant lesions of colon polyps. It is of great clinical significance to use computer-aided diagnosis system to automatically and accurately segment colon polyps, which can help doctors improving the detection rate of polyps during colonoscopy. Nowadays, deep learning technology is widely used in medical image segmentation, and the colon polyp segmentation algorithm based on deep learning has also made significant progress. Firstly, the traditional polyp segmentation algorithm and its advantages and limitations are briefly introduced. Secondly, the deep learning polyp segmentation algorithm is reviewed in three aspects：segmentation model based on classical CNN structure, U-Net structure, and multi-model fusion, then the improvement strategy of the algorithm and its advantages and limitations is summarized. The public datasets of the colon polyp image and the data preprocessing methods are concluded. Finally, the challenges of polyp segmentation based on deep learning are summarized, and the future research direction in this field is prospected. Reference | Related Articles | Metrics

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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 Abstract （264）      PDF（pc） （692KB）（207）       Knowledge map    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. Reference | Related Articles | Metrics

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Baggage Tracking Technology Based on Improved YOLO v8 CAO Chao, GU Xingsheng Computer Engineering and Applications    2024, 60 (9): 151-158.   DOI: 10.3778/j.issn.1002-8331.2310-0238 Abstract （135）      PDF（pc） （6479KB）（207）       Knowledge map    Save In the airport baggage sorting scenario, the traditional multi-target tracking algorithm has the problems of high target ID switching rate and high false alarm rate of target trajectory. This paper presents a baggage tracking technique based on improved YOLO v8 and ByteTrack algorithms. The CBATM module is added, the ADH decoupling head is replaced and the loss function during training is changed, the detection accuracy is increased, the discrimination of target features is strengthened, and the ID switching rate of the target is reduced. GSI interpolation processing in Byte data association, which not only uses high box and low box, but also ensures the tracking effect after a long time of occlusion, and reduces the ID error switching caused by occlusion. In the airport baggage sorting dataset, MOTA and IDF 1 reach 89.9% and 90.3%, respectively, which show a significant improvement and can steadily realize the tracking of luggage ID. Reference | Related Articles | Metrics

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Research Progress of Image Style Transfer Based on Neural Network LIAN Lu, TIAN Qichuan, TAN Run, ZHANG Xiaohang Computer Engineering and Applications    2024, 60 (9): 30-47.   DOI: 10.3778/j.issn.1002-8331.2309-0204 Abstract （154）      PDF（pc） （7029KB）（207）       Knowledge map    Save Image style transfer is the process of remapping the content of a specified image with a style image, which is a research hotspot in the field of artificial intelligence computer vision. Traditional image style transfer methods are mainly based on the synthesis of physical and texture techniques, and the style transfer effect is rough and less robust. With the emergence of image datasets and the proposal of various deep learning model networks, many models and algorithms for image style transfer have emerged. This paper analyzes the current status of image style transfer research, combs the development of image style transfer and the latest research progress, and gives the future research directions of image style transfer through comparative analysis. Reference | Related Articles | Metrics

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Improved YOLOv8 Object Detection Algorithm for Traffic Sign Target TIAN Peng, MAO Li Computer Engineering and Applications    2024, 60 (8): 202-212.   DOI: 10.3778/j.issn.1002-8331.2309-0415 Abstract （252）      PDF（pc） （937KB）（207）       Knowledge map    Save Although the current testing technology is becoming increasingly mature, the detection of small targets in complex environments is still the most difficult point in research. Aiming at the problem of high target proportion of traffic signs in road traffic scenarios, the problem of high target proportion of small targets and large environmental interference factors, it proposes a type of road traffic logo target test algorithm based on YOLOv8 improvement. Due to the prone to missed inspection in small target testing, the bi-level routing attention (BRA) attention mechanism is used to improve the network’s perception of small targets. In addition, it also uses a shape-changing convolutional module deformable convolution V3 (DCNV3). It has a better feature extraction ability for irregular shapes in the feature map, so that the backbone network can better adapt to irregular space structures, and pay more accurately to important attention，objectives, thereby improving the detection ability of the model to block the overlapping target. Both DCNV3 and BRA modules improve the accuracy of the model without increasing the weight of the model. At the same time, the Inner-IOU loss function based on auxiliary border is introduced. On the four data sets of RoadSign, CCTSDB, TSDD, and GTSDB, small sample training, large sample training, single target detection, and multi-target detection are performed. The experimental results are improved. Among them, the experiments on the RoadSign data set are the best. The average accuracy of the improved YOLOv8 model mAP50 and mAP50：95 reaches 90.7% and 75.1%, respectively. Compared with the baseline model, mAP50 and mAP50：95 have increased by 5.9 and 4.8 percentage points, respectively. The experimental results show that the improved YOLOV8 model effectively implements the traffic symbol detection in complex road scenarios. Reference | Related Articles | Metrics

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Multi-Object Tracking Algorithm Based on CNN-Transformer Feature Fusion ZHANG Yingjun, BAI Xiaohui, XIE Binhong Computer Engineering and Applications    2024, 60 (2): 180-190.   DOI: 10.3778/j.issn.1002-8331.2211-0028 Abstract （345）      PDF（pc） （787KB）（202）       Knowledge map    Save In convolutional neural network (CNN), convolution can efficiently extract local features of the object, but it is difficult to capture global representation; in the visual Transformer, the attention mechanism can capture long-distance feature dependency, but will ignore local feature details. To solve the above problems, a multi-object tracking algorithm CTMOT (CNN transformer multi-object tracking) based on CNN-Transformer hybrid network for feature extraction and fusion is proposed. Firstly, the backbone network is adopted based on CNN and Transformer to extract the local and global features of the image respectively. Secondly, two way bridge module (TBM) is used to fully integrate two features. Then, the fused features are input to two parallel decoders for processing. Finally, the detection box and the tracking box outputted by the decoder are matched to obtain final tracking result and complete the multi-target tracking task. Evaluated on MOT17, MOT20, KITTI and UA-DETRAC multi-object tracking datasets, the MOTA indicators of CTMOT algorithm have reached 76.4%, 66.3%, 92.36% and 88.57% respectively. It is equivalent to the SOTA method on the MOT dataset, and achieves the SOTA effect on the KITTI dataset. At the same time, the MOTP and IDs indicators have reached the SOTA effect on all datasets. In addition, since the object detection and correlation are completed at the same time, the object tracking can be carried out end-to-end, and the tracking speed can reach 35 FPS, which shows that CTMOT algorithm achieves a good balance in the real-time and accuracy of tracking, and has great potential. Reference | Related Articles | Metrics

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Survey of Agricultural Knowledge Graph TANG Wentao, HU Zelin Computer Engineering and Applications    2024, 60 (2): 63-76.   DOI: 10.3778/j.issn.1002-8331.2305-0203 Abstract （270）      PDF（pc） （629KB）（201）       Knowledge map    Save Knowledge graphs are a key technology in the era of big data, specifically for knowledge engineering. Utilizing the powerful semantic understanding and knowledge organization capabilities of knowledge graphs, issues such as scattered and disordered agricultural knowledge, and insufficient coverage of knowledge in the construction of modern agriculture can be resolved. Firstly, considering the complexity and specialty of agricultural data, the construction methods and framework of agricultural knowledge graphs are introduced. Secondly, the current domestic and international research status of the four key technologies in the construction of agricultural knowledge graphs-ontology construction, knowledge extraction, knowledge fusion, and knowledge reasoning are reviewed. Furthermore, the systematic applications of agricultural knowledge graphs in decision support, intelligent question-answering systems, and recommendation systems are sorted out. Lastly, several specific instances of agricultural knowledge graphs are presented. Based on the current status of research on agricultural knowledge graphs, prospects for its future research directions are offered. Reference | Related Articles | Metrics