基于YOLOv5的网球检测,使用tensorrt
1College of Physical Education, Jilin Normal University, Siping, 136000, China.
Scientific reports
|July 2, 2025
概括
本研究介绍了一种先进的眼检测系统,用于网球,利用YOLOv5和TensorRT. 这种新方法显著提高了网球丢失检测的准确性和速度,以实现公平比赛的处罚.
科学领域:
- 运动技术技术 运动技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 网球处罚需要高度准确和公平.
- 目前用于检测球滴的方法可能缓慢且不准确.
- 需要技术解决方案来改善网球的裁判.
研究的目的:
- 为网球开发一个准确和快速的眼检测系统.
- 为了利用深度学习进行实时球跟踪和检测.
- 为网球比赛裁判提供可靠的技术支持.
主要方法:
- 利用YOLOv5进行高效的网球特征提取和目标检测.
- 集成的TensorRT用于推理加速,优化实时性能.
- 在TensorRT框架内实施了层融合和内存优化技术.
主要成果:
- 在网球检测中达到94%的平均精度.
- 记录了0.39米的综合检测错误.
- 证明了检测的最低计算时间为2.28秒.
结论:
- 拟议的眼检测方法显著提高了网球摔落检测的准确性和速度.
- 该系统提供可靠的技术支持,在网球比赛中提供公平有效的处罚.
- 整合YOLOv5和TensorRT为实时体育分析提供了一个强大的解决方案.
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