Lightweight CA-YOLOv7-Based Badminton Stroke Recognition: A Real-Time and Accurate Behavior Analysis Method
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| 出版年: | International Journal of Advanced Computer Science and Applications vol. 16, no. 2 (2025) |
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| 第一著者: | |
| 出版事項: |
Science and Information (SAI) Organization Limited
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| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full Text - PDF |
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|---|---|---|---|
| 001 | 3180200304 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2158-107X | ||
| 022 | |a 2156-5570 | ||
| 024 | 7 | |a 10.14569/IJACSA.2025.0160235 |2 doi | |
| 035 | |a 3180200304 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a PDF | |
| 245 | 1 | |a Lightweight CA-YOLOv7-Based Badminton Stroke Recognition: A Real-Time and Accurate Behavior Analysis Method | |
| 260 | |b Science and Information (SAI) Organization Limited |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a With the rapid development of sports technology, accurate and real-time recognition of badminton stroke postures has become essential for athlete training and match analysis. This study presents an improved YOLOv7-based method for badminton stroke posture recognition, addressing limitations in accuracy, real-time performance, and automation. To optimize the model, pruning techniques were applied to the backbone structure, significantly enhancing processing speed for real-time demands. A parameter-free attention module was integrated to improve feature extraction without increasing model complexity. Furthermore, key stroke action nodes were defined, and a joint point matching module was introduced to enhance recognition accuracy. Experimental results show that the improved model achieved a mAP@0.5 of 0.955 and a processing speed of 44 frames per second, demonstrating its capability to deliver precise and efficient badminton stroke recognition. This research provides valuable technical support for coaches and athletes, enabling better analysis and optimization of stroke techniques. | |
| 653 | |a Modules | ||
| 653 | |a Athletes | ||
| 653 | |a Real time | ||
| 653 | |a Recognition | ||
| 653 | |a Optimization | ||
| 653 | |a Teaching | ||
| 653 | |a Motion capture | ||
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Computer science | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Sports training | ||
| 653 | |a Automation | ||
| 653 | |a Coaches & managers | ||
| 653 | |a Feedback | ||
| 653 | |a Badminton | ||
| 653 | |a Processing speed | ||
| 653 | |a Efficiency | ||
| 773 | 0 | |t International Journal of Advanced Computer Science and Applications |g vol. 16, no. 2 (2025) | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3180200304/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3180200304/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |