SCB-YOLOv5:用于运动员规范运动的轻量级智能检测模型
Qing Du1, Lian Tang2,3, Ya Li4
1School of Resource Environment and Safety Engineering, University of South China, Hengyang, 421001, China.
Scientific reports
|April 14, 2024
概括
通过改进YOLOv5算法,SCB-YOLOv5增强了运动员动作检测. 这种新的方法实现了更高的精度和回忆,用于检测标准化体操运动员的运动,帮助体育指导.
科学领域:
- 计算机视觉 计算机视觉
- 体育科学 运动科学 运动科学
- 机器学习 机器学习
背景情况:
- 智能检测运动员行为对于有效的体育指导至关重要.
- 现有的目标检测算法在实际,大规模应用中面临挑战.
- 在体操中标准化的运动检测需要强大的和高效的算法.
研究的目的:
- 开发一个改进的物体检测模型,SCB-YOLOv5,用于检测标准化体操运动员的运动.
- 提高体育运动员行为分析的效率和准确性.
- 解决现有的现实场景中大规模目标检测算法的局限性.
主要方法:
- 收集和标记有氧运动运动员的运动,以创建规范性行为数据集.
- 使用Mosaic9技术扩展了数据集,以提高稳定性.
- 用ShuffleNet V2和卷积块注意模块 (SCB) 重建了YOLOv5骨干,以减少参数并保持特征提取.
- 集成了一个加权的双向特征金字塔网络,用于精确的多尺度特征融合.
主要成果:
- 与YOLOv5.5相比,SCB-YOLOv5实现了56.9%的模型尺寸缩小.
- 显示了93.7%的检测精度,99%的回忆和94.23%的mAP.
- 与原来的YOLOv5算法相比,其性能提高了3.53%.
结论:
- SCB-YOLOv5有效地满足了对现场运动员动作检测的要求.
- 拟议的模型为体操运动运动分析提供了更轻,更准确的解决方案.
- 这些发现支持SCB-YOLOv5在指导体育指导和绩效评估中的应用.
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