足球裁判的手势识别算法基于YOLOv8s
Zhiyuan Yang1, Yuanyuan Shen1, Yanfei Shen1
1School of Sport Engineering, Beijing Sport University, Beijing, China.
Frontiers in computational neuroscience
|March 5, 2024
概括
这项研究引入了用于足球裁判手势识别 (FRGR) 的增强深度学习模型,提高了复杂比赛环境中的准确性. 优化的模型显著优于现有的自动化手势解释方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 运动技术技术 运动技术
背景情况:
- 由于各种手势和环境干扰,自动足球裁判手势识别 (FRGR) 具有挑战性.
- 现有的视觉传感器方法在FRGR任务中经常产生不满意的性能.
研究的目的:
- 开发一个改进的深度学习模型,用于准确的足球裁判手势识别.
- 使用新的优化策略,解决当前FRGR方法的局限性.
主要方法:
- 基于YOLOv8s的深度学习模型被开发出来,结合了全球注意力机制 (GAM) 来集中注意力于手势.
- 集成P2检测头用于增强小物体检测,并采用了最小点距离交叉在欧盟 (MPDIoU) 损失函数.
- 在一组数据集上进行了实验,其中包括1200张图像,其中包括六种不同的裁判手势.
主要成果:
- 拟议的模型实现了89.3%的精度,88.9%的回忆,89.9%的mAP@0.5和77.3%的mAP@0.5:0.95.
- 与最新的YOLOv8相比,观察到1.4% (精度),2.0% (回忆),1.1% (mAP@0.5) 和5.4% (mAP@0.5:0.95) 的性能改善.
- 该模型与七个现有模型和10个优化变体相比,表现出更高的性能.
结论:
- 开发的深度学习模型为自动足球裁判手势识别提供了一个有前途的解决方案.
- 集成GAM,P2检测头和MPDIoU损失函数有效地提高了FRGR的准确性.
- 该方法在改善足球比赛中的沟通和裁判方面显示出实践应用的巨大潜力.
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