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Transformer-Based Person Re-Identification: A Comprehensive Review

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IEEE Transactions on Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles 2024年第7期9卷 1-19页

作者： Sarker, Prodip Kumar Zhao, Qingjie Uddin, Md. Kamal School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Bangladesh

In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of accurately matching and identifying persons across several camera views that do not overlap with one another. This is of utmost importance to video surveillance, public safety, and person-tracking applications. However, vision-related difficulties, such as variations in appearance, occlusions, viewpoint changes, cloth changes, scalability, limited robustness to environmental factors, and lack of generalizations, still hinder the development of reliable person re-ID methods. There are few approaches have been developed based on these difficulties relied on traditional deep-learning techniques. Nevertheless, recent advancements of transformer-based methods, have gained widespread adoption in various domains owing to their unique architectural properties. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, there are few studies that consider transformer-based techniques for further investigation. This review proposes recent literature on transformer-based approaches, examining their effectiveness, advantages, and potential challenges. This review is the first of its kind to provide insights into the revolutionary transformer-based methodologies used to tackle many obstacles in person re-ID, providing a forward-thinking outlook on current research and potentially guiding the creation of viable applications in real-world scenarios. The main objective is to provide a useful resource for academics and practitioners engaged in person re-ID. IEEE

关键词： Cameras

Underwater Target Detection Based on Improved YOLOv7

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IAENG International Journal of computer science 2024年第4期51卷 422-429页

作者： Fu, Junshang Tian, Ying School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning 114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan 114051 China

Underwater target detection is an important part of marine exploration. However, in complex underwater environments due to factors like light absorption and scattering, as well as variations in water quality and clarity. These challenges result in inaccurate target feature extraction, sluggish detection speeds, and insufficient robustness in the detection methods. In order to address these issues, an enhanced YOLOv7 network (YOLOv7-SPNW-D) is proposed for underwater target detection in this study. The SPD-MP module structure replaces the MP module in the neck network to capture small targets and enhance detection accuracy. A novel NWD loss function is employed to facilitate smoother extraction of small target features. This enhances feature extraction and improves network inference speed. Additionally, incorporating a small target detection module enables the providing of more comprehensive small target information within a deep feature map. This, in turn, improves the capture of small target features in complex backgrounds, and avoids feature loss and enhancing model exactness. Through ablation experiments on the URPC dataset, it is shown that the improved YOLOv7-SPNW-D algorithm performs better than the original YOLOv7 algorithm, with the mAP50 value increased to 87.0%, proving the effectiveness of this method. In conclusion, the improved YOLOv7-SPNW-D model is more suitable for underwater marine organism target detection. © (2024) International Association of Engineers.

关键词： Water quality

Small Object Detection in Aerial Drone Imagery based on YOLOv8

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IAENG International Journal of computer science 2024年第9期51卷 1346-1354页

作者： Pan, Junyu Zhang, Yujun School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In recent years, the utilization of unmanned aerial vehicles (UAVs) for aerial target detection has gained significant attention due to their high-altitude perspective and maneuverability, which offer novel opportunities and tremendous potential in this field. However, detecting targets in UAV aerial images remains highly challenging due to the presence of numerous small targets with limited feature information, as well as issues like target occlusion and complex backgrounds that severely impact detection accuracy. To address these challenges, we propose a detection model called BDC-YOLOv8 that aims to enhance accuracy for small targets while minimizing computational complexity. Specifically, we augment the YOLOv8 architecture by incorporating a dedicated detection head tailored for small targets to improve performance when encountering such objects. Additionally, we restructure the neck network of the model to better extract and fuse feature information from targets with significant scale variations. Furthermore, we introduce the concept of DynamicHead to enhance the detection head by incorporating various attention mechanisms suitable for our task ahead of the original detection head, thereby enhancing the model’s capability to detect objects of different scales and complex backgrounds. Moreover, we introduce Convolutional Block Attention Module (CBAM) to identify regions of interest in densely populated areas. Extensive experiments conducted on the VisDrone2019 dataset yield promising results where our model achieves a mean Average Precision (mAP) score of 38% and an AP50 score of 59.6%. Compared to the original YOLOV8 model, improvements are observed with increases in mAP by 2.5% and AP50 by 3.7%, respectively. Notably, our model demonstrates a significant enhancement in detecting small targets with an increase in APs evaluation metric by 4.1%. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Aerial photography

Secure vehicular data communication in Named Data Networking

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Digital Communications and Networks 2023年第1期9卷 203-210页

作者： Xiaonan Wang Xilan Chen Xingwei Wang School of Computer Science and Engineering Changshu Institute of TechnologyChangshu215500China School of Computer Science and Technology Northeastern UniversityShenyang110169China

Vehicular data misuse may lead to traffic accidents and even loss of life,so it is crucial to achieve secure vehicular data *** paper focuses on secure vehicular data communications in the Named Data Networking(NDN).In NDN,names,provider IDs and data are transmitted in plaintext,which exposes vehicular data to security threats and leads to considerable data communication costs and failure *** paper proposes a Secure vehicular Data Communication(SDC)approach in NDN to supress data communication costs and failure *** constructs a vehicular backbone to reduce the number of authenticated nodes involved in reverse *** the ciphtertext of the name and data is included in the signed Interest and Data and transmitted along the backbone,so the secure data communications are *** is evaluated,and the data results demonstrate that SCD achieves the above objectives.

关键词： Named data networking Reverse path Secure data communication

Steel Surface Defect Detection Algorithm Based on S-YOLOv8

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IAENG International Journal of computer science 2025年第3期52卷 644-652页

作者： Zhang, Xu Cui, Wenhua Tao, Ye Shi, Tianwei School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

Steel, being a widely utilized material in industrial production, holds a pivotal role in ensuring product safety and longevity. Hence, the exploration and implementation of steel surface defect detection technology carry significant importance. This paper introduces a steel surface defect detection algorithm based on S-YOLOv8. The algorithm, rooted in YOLOv8n as a benchmark model, initially incorporates a shift-wise shift operator in the backbone network. This introduction notably enhances accuracy compared to conventional CNN models while markedly reducing computational demands. Furthermore, the utilization of the SF-Neck framework, integrating the scale sequence feature fusion module (SSFF) and triple feature encoder module (TFE) in the head network, enriches the network’s multi-scale information extraction capabilities. Subsequently, the adoption of the WIoU loss function enhances the overall detector performance. Lastly, the integration of the SEAM occlusion attention module refines the detection head segment of the YOLOv8 algorithm, effectively addressing defect occlusion challenges. Experiments conducted on the NEU-DET dataset reveal that the mAP value of the S-YOLOv8 model reaches an impressive 84.2%. Comparative analysis with other mainstream algorithms demonstrates a substantial enhancement in detection accuracy, alongside a reduction in instances of leakage and misdetection. Consequently, this study charts a new technical trajectory for quality control within the steel manufacturing industry. © (2025), (International Association of Engineers). All rights reserved.

关键词： Benchmarking

Large Kernel Disassembling Attention Mechanism for Remote Sensing Object Detection

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IAENG International Journal of computer science 2024年第9期51卷 1367-1373页

作者： Geng, Y.X. Wang, L. Wang, Y.G. School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

In recent years, remote sensing object detection has become a research hotspot in computer vision tasks. However, previous approaches for remote sensing object detection often overlook the rich contextual information in images, which is crucial for accurately detecting occluded or interconnected objects using convolutional neural networks. To capture this contextual information, we propose a method called the Large Kernel Disassembling (LKD) Attention Mechanism. LKD breaks down large convolutional kernels to provide a larger receptive field to the convolutional neural networks, enabling them to capture rich contextual information in remote sensing images and enhance their performance. We employ an adaptive channel submodule and a deep convolutional spatial submodule. The adaptive channel submodule helps the network learn relationships between different channels, while the deep convolutional spatial submodule aids in extracting rich spatial features. We evaluate the proposed attention mechanism on the DIOR dataset and compare it with several recent attention mechanisms on the SSDD dataset. Experimental results demonstrate the superiority of LKD in terms of performance over other methods, validating the effectiveness of the Large Kernel Disassembling attention mechanism in remote sensing object detection tasks. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Convolutional neural networks

Research on Steel Surface Defects Detection Algorithms by YOLOv8 Based on Attention Mechanism

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IAENG International Journal of computer science 2024年第9期51卷 1309-1315页

作者： Gao, ShiQuan Tian, Ying School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

As deep learning advances, neural network technologies are increasingly penetrating the field of steel surface defect detection. To tackle the challenges of low accuracy and inadequate quality, we introduce CMS-YOLOv8s, a defect detection algorithm engineered for superior precision and efficiency. Initially, integrate the CBAM to enhance the network's focus on relevant information, allowing it to leverage multi-dimensional data like spatial and channel dimensions fully. Next, introduce the SPPFCSPC module to fuse feature information from different scales, expanding the model's receptive field and enhancing its feature extraction abilities. Finally, incorporate a small object detection head to boost the model's capability in identifying tiny targets, strengthening its ability to detect targets across different scales. Experimental findings show that CMS-YOLOv8s attains a detection accuracy of 70.4%, registering a significant 3.3% enhancement compared to the original YOLOv8s. This advancement distinctly improves defect detection accuracy across all categories. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Surface defects

Detection of Small Underwater Organisms Based on Improved YOLOv8

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IAENG International Journal of computer science 2024年第8期51卷 1020-1026页

作者： Miao, Liheng Tian, Ying School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

The underwater environment is complex and diverse, making it challenging to locate aquatic organisms accurately. The precise identification of underwater animals is crucial for ecological research and fisheries management. Addressing the issue of inaccurate localization of small underwater targets, this study introduces a novel model, YOLOv8-2PCC, based on the YOLOv8 algorithm with improvements. First, to improve the efficiency of the YOLOv8 network, the C2F module in the original YOLOv8 network model was replaced with convolution to reduce the computational load of the model. Secondly, the up-sampling operator CARAFE is employed, which excels in capturing features at various scales. Finally, a small target detection layer has been incorporated to extract additional shallow features, effectively enhancing the model's ability to detect small targets. Utilizing the URPC dataset for training and testing, the results indicate that our proposed algorithm achieves a mean Average Precision (mAP) of 85.9%. Compared to YOLOv8n, there is a 4.4% improvement, effectively enhancing the accuracy of underwater organism detection in complex underwater environments. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Aquatic organisms

Research on Social Recommendation Algorithm Based on PSO_KFCM Clustering and CBAM Attention Mechanism of Graph Neural Networks

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IAENG International Journal of computer science 2024年第8期51卷 936-948页

作者： Teng, Yue Yang, Kai School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In today's society, people increasingly need information acquisition due to the rapid development of science and technology and the consequent increase in available data. However, finding the information users need from this vast data has become challenging. To tackle this problem, recommending preferred information to users is becoming increasingly important. However, accurately recommending information by analyzing existing models such as GraphRec is still a challenging problem. A method called PSO_KFCM is proposed in this paper to solve this problem better. The technique combines Particle Swarm Optimization (PSO) with hybrid optimization and the kernel fuzzy C-means clustering technique to cluster similar recommendation data into one class. This way, the complexity and randomness of the recommendation data are reduced. It improves the speed and accuracy of the model prediction, which lays a solid foundation for the subsequent recommendation. Various factors will impact the recommendation process, and channel and spatial characteristics are essential. CBAM attention is added to the original attention mechanism to fully utilize these features in the recommendation data to enhance its performance. Furthermore, this paper proposes a social recommendation prediction method that combines CBAM attention and PSO_KFCM clustering and introduces a new social model called TTYGNN. The TTYGNN model optimizes the recommendation effect while maintaining the original advantages, enabling users to obtain the required information more quickly and accurately. To verify the effectiveness and practicality of the proposed model, extensive experimental comparisons were conducted on two widely used datasets. The results show that the TTYGNN model outperforms similar methods in all indicators, proving its superiority in information recommendation. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Fuzzy clustering