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Semi-supervised Lightweight Object Detection Method Based on Improved YOLOv7-tiny 25

Semi-supervised Lightweight Object Detection Method Based on...

引用

Proceedings of the 2025 3rd International Conference on Communication Networks and Machine Learning

作者： Zhilong Gao Chunsheng Hua Institute of Intelligent Robots and Pattern Recognition Liaoning University Shenyang Liaoning China College of Information Liaoning University Shenyang Liaoning China

ISBN: (纸本)9798400713231

Challenges in target detection include significant variations in target size, insufficient detection accuracy on small, resource-constrained devices, and the high cost of fully annotating large datasets. To address these issues, a semi-supervised lightweight target detection method based on the improved YOLOv7-tiny algorithm is proposed. First, a new spatial pyramid pooling network, MSPP, is designed to address the problem of significant variations in target size. Then, a lightweight convolutional attention module, GC-SAM, is introduced, which generates stronger feature representations by combining channel and spatial attention mechanisms. Finally, a multi feature semi-supervised data filtering method is proposed, which enhances the model's ability to learn valuable information from unlabeled data, effectively improving detection accuracy. Experimental results show that the improved algorithm achieves a detection accuracy of 82.2% and 63.6% for mAP@50 on 30% of the KITTI and SODA1000 datasets, which is 5.4% and 2.7% higher than the original model, respectively. The model size is only 14.4 MB and the detection speed is 82.1 frames per second, confirming the effectiveness of the algorithm.

关键词： Object Detection

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TriFusionNet: Comprehensive Adaptation to Viewpoint Changes and Physiological Information Integration in 3D-Based Person Re-Identification 7

TriFusionNet: Comprehensive Adaptation to Viewpoint Changes ...

引用

7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Hua, Chunsheng Zhang, Wenqian Zhao, Xiaoheng Zhang, Hao Institute Of Intelligent Robots And Pattern Recognition College Of Information Liaoning University Shenyang China

ISBN: (纸本)9798350361445

This study introduces an innovative person re-identification method, TriFusionNet, which cleverly combines 3D reconstruction image features with data augmentation techniques, significantly enhancing performance. We incorporate a 'Physiological Information Feature Extraction Branch' to comprehensively capture both the body structure and motion information of persons. The primary challenge addressed is the longstanding negative impact of viewpoint changes on accuracy in person re-identification. Our approach enables the synthesis of more robust and identifiable features in new views. Furthermore, to comprehensively capture person features, we introduce a physiological information feature extraction branch that extracts 15 authentic physiological features from action sequences. These features not only contribute to accuracy but also deepen the understanding of person body status and motion characteristics. The uniqueness of this research lies in going beyond traditional data augmentation methods. Through 3D reconstruction and feature fusion, we address occlusion issues and, through keyframe selection and multi-angle feature fusion, diversify training data more effectively, preserving the body structure and motion information of persons. In summary, TriFusionNet introduces innovative thinking to person re-identification by integrating 3D reconstruction, feature fusion, and physiological information feature extraction. It not only improves performance but also establishes a foundation for more comprehensive extraction of person features, addressing challenges faced by traditional methods. © 2024 IEEE.

关键词： Extraction

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3D Human Pose Estimation with Bone Symmetry and Motion Locality 3

3D Human Pose Estimation with Bone Symmetry and Motion Local...

引用

3rd International Conference on Electronics and Information Technology, EIT 2024

作者： Zhang, Xinyu Hua, Chunsheng Liaoning University Institute of Intelligent Robots and Pattern Recognition College of Information Liaoning Shenyang110036 China

ISBN: (纸本)9798350369151

The bone symmetry and motion locality are two important priors for 3D human pose estimation. For bone symmetry, symmetric bones typically have equal length and higher motion correlation. In terms of motion locality, the motion of the human body conforms to its structure and is temporally continuous. These two priors align with human intuition, but are challenging for Transformer to directly abstract them from data. We propose novel modules, Bone Symmetry Temporal Transformer (BSTT) and Spatial-Temporal Locality Fusion (STLF), to utilize these two priors, and design a new Transformer-based BSMLFormer model. Specifically, BSTT considers the temporal sequences of two symmetric bones as a single new sequence and calculates its global relationships by self-attention mechanism. STLF performs local computations by GCN and convolutions to capture the local dependencies in adjacent joints and adjacent parts. The local-global alternating structure of BSMLFormer allows for consideration of the local information during global calculations. More remarkably, BSMLFormer achieves the state-of-the-art performance with 39.0mm MPJPE on Human3.6M dataset. © 2024 IEEE.

关键词： Motion estimation

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3D Human Pose Estimation with Bone Symmetry and Motion Locality

3D Human Pose Estimation with Bone Symmetry and Motion Local...

引用

International Conference on Electronics and Information Technology (EIT)

作者： Xinyu Zhang Chunsheng Hua Institute of Intelligent Robots and Pattern Recognition College of Information Liaoning University Shenyang China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369168

关键词： Three-dimensional displays Correlation Computational modeling Pose estimation Stacking Bones Transformers Joints Information technology

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TriFusionNet: Comprehensive Adaptation to Viewpoint Changes and Physiological Information Integration in 3D-Based Person Re-Identification

TriFusionNet: Comprehensive Adaptation to Viewpoint Changes ...

引用

Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Chunsheng Hua Wenqian Zhang Xiaoheng Zhao Hao Zhang Institute of Intelligent Robots and Pattern Recognition College of Information Liaoning University Shenyang P.R. China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

This study introduces an innovative person re-identification method, TriFusionNet, which cleverly combines 3D reconstruction image features with data augmentation techniques, significantly enhancing performance. We incorporate a “Physiological Information Feature Extraction Branch” to comprehensively capture both the body structure and motion information of persons. The primary challenge addressed is the longstanding negative impact of viewpoint changes on accuracy in person re-identification. Our approach enables the synthesis of more robust and identifiable features in new views. Furthermore, to comprehensively capture person features, we introduce a physiological information feature extraction branch that extracts 15 authentic physiological features from action sequences. These features not only contribute to accuracy but also deepen the understanding of person body status and motion characteristics. The uniqueness of this research lies in going beyond traditional data augmentation methods. Through 3D reconstruction and feature fusion, we address occlusion issues and, through keyframe selection and multi-angle feature fusion, diversify training data more effectively, preserving the body structure and motion information of persons. In summary, TriFusionNet introduces innovative thinking to person re-identification by integrating 3D reconstruction, feature fusion, and physiological information feature extraction. It not only improves performance but also establishes a foundation for more comprehensive extraction of person features, addressing challenges faced by traditional methods.

关键词： Three-dimensional displays Control engineering Training data Feature extraction Data augmentation Physiology Data mining

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