SD-YOLOv8: Automated Motion Detection System for Aerobics Students
Existing object detection algorithms are often constrained by the high computational costs associated with large network structures in practical applications. To facilitate the development of intelligent physical education classrooms, particularly for aerobic exercise student movement recognition, w...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11069293/ |
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| author | Lian Tang Ya Li Qing Du Jincheng Liang |
| author_facet | Lian Tang Ya Li Qing Du Jincheng Liang |
| author_sort | Lian Tang |
| collection | DOAJ |
| description | Existing object detection algorithms are often constrained by the high computational costs associated with large network structures in practical applications. To facilitate the development of intelligent physical education classrooms, particularly for aerobic exercise student movement recognition, we propose a lightweight student action recognition system based on SD-YOLOv8. First, we constructed a specialized dataset by capturing and annotating aerobic exercise student movement images. Then, based on the YOLOv8 network architecture, we implemented two key improvements: 1) introducing Selective Kernel Networks (SKNet) in the multi-scale feature fusion layer to enhance local feature capture capabilities; 2) replacing the original detection head with Dynamic Head to effectively reduce the computational burden while improving detection accuracy and efficiency. Finally, we developed a user-friendly graphical interface using the PyQt framework, enabling visual deployment and real-time interaction with the model. Experimental results demonstrate that the improved SD-YOLOv8 achieves 97.6%, mean Average Precision (mAP) on the aerobic exercise student behavior recognition dataset, representing a 3.5% improvement over the original model. The system shows significant advantages in enhancing action recognition accuracy and real-time feedback, providing robust technical support for physical education instruction. |
| format | Article |
| id | doaj-art-c9cef5357feb4fd5a1d272876ccbb020 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c9cef5357feb4fd5a1d272876ccbb0202025-08-20T03:16:56ZengIEEEIEEE Access2169-35362025-01-011311805511806510.1109/ACCESS.2025.358556111069293SD-YOLOv8: Automated Motion Detection System for Aerobics StudentsLian Tang0https://orcid.org/0009-0000-6054-4171Ya Li1Qing Du2https://orcid.org/0009-0000-2541-0442Jincheng Liang3https://orcid.org/0009-0004-7046-7199School of Sports Science and Engineering, Hunan Engineering University, Xiangtan, ChinaSchool of Electrical Information Engineering, Hunan Engineering University, Xiangtan, ChinaSchool of Resources Environment and Safety Engineering, University of South China, Hengyang, ChinaSchool of Resources Environment and Safety Engineering, University of South China, Hengyang, ChinaExisting object detection algorithms are often constrained by the high computational costs associated with large network structures in practical applications. To facilitate the development of intelligent physical education classrooms, particularly for aerobic exercise student movement recognition, we propose a lightweight student action recognition system based on SD-YOLOv8. First, we constructed a specialized dataset by capturing and annotating aerobic exercise student movement images. Then, based on the YOLOv8 network architecture, we implemented two key improvements: 1) introducing Selective Kernel Networks (SKNet) in the multi-scale feature fusion layer to enhance local feature capture capabilities; 2) replacing the original detection head with Dynamic Head to effectively reduce the computational burden while improving detection accuracy and efficiency. Finally, we developed a user-friendly graphical interface using the PyQt framework, enabling visual deployment and real-time interaction with the model. Experimental results demonstrate that the improved SD-YOLOv8 achieves 97.6%, mean Average Precision (mAP) on the aerobic exercise student behavior recognition dataset, representing a 3.5% improvement over the original model. The system shows significant advantages in enhancing action recognition accuracy and real-time feedback, providing robust technical support for physical education instruction.https://ieeexplore.ieee.org/document/11069293/Intelligent sportsintelligent classroommotion recognitionYOLOv8 |
| spellingShingle | Lian Tang Ya Li Qing Du Jincheng Liang SD-YOLOv8: Automated Motion Detection System for Aerobics Students IEEE Access Intelligent sports intelligent classroom motion recognition YOLOv8 |
| title | SD-YOLOv8: Automated Motion Detection System for Aerobics Students |
| title_full | SD-YOLOv8: Automated Motion Detection System for Aerobics Students |
| title_fullStr | SD-YOLOv8: Automated Motion Detection System for Aerobics Students |
| title_full_unstemmed | SD-YOLOv8: Automated Motion Detection System for Aerobics Students |
| title_short | SD-YOLOv8: Automated Motion Detection System for Aerobics Students |
| title_sort | sd yolov8 automated motion detection system for aerobics students |
| topic | Intelligent sports intelligent classroom motion recognition YOLOv8 |
| url | https://ieeexplore.ieee.org/document/11069293/ |
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