TAR-YOLO:一个新的深度学习模型和数据集用于网球动作识别
Bohan Chen1, Liangyu Du2, Weichen Fang3
1Tennis College, Wuhan Sports University, Wuhan, Hubei, China.
Scandinavian journal of medicine & science in sports
|December 10, 2025
概括
这项研究引入了网球动作识别你只看一次检测网络 (TAR-YOLO),用于准确的网球动作识别. 这种新型模型在动态的体育环境中增强了人工智能辅助的教练和技能评估.
科学领域:
- 计算机视觉 计算机视觉
- 运动科学 运动科学 运动科学
- 人工智能的人工智能
背景情况:
- 全球网球越来越受欢迎,需要智能系统来识别和反动作.
- 传统的方法缺乏精确度,无法在网球中培养细粒度的技能.
- 挑战包括闭塞,姿势变形和网球行动中的多视图一致性.
研究的目的:
- 为网球开发一种新的,姿势驱动的动作识别模型.
- 解决现有方法在识别复杂的网球动作方面的局限性.
- 为了引入网球行动识别你只看一次检测网络 (TAR-YOLO).
主要方法:
- 在YOLO11架构的基础上开发了TAR-YOLO.
- 建议的新型组件:RES-Head用于多尺度特征融合和DSAM用于增强注意力.
- 集成的SPD-Conv用于改进特征提取和样本不平衡的滑动损失.
- 构建了一个自定义的数据集,TAR-Det,用于网球姿势估计和动作分类.
主要成果:
- 在TAR-Det数据集上,TAR-YOLO实现了高性能.
- 关键指标包括95.4%的精度,93.7%的回忆和96.2%的mAP<0.5>.
- 经过证明的效率为16.9 FLOP和89.3 FPS.
结论:
- TAR-YOLO有效地识别了复杂和动态的网球动作.
- 该模型显示了AI辅助辅导和实时广播等应用的巨大潜力.
- 建筑改进提高了运动动作识别的准确性和效率.
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