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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11069293/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |