基准测试YOLOv8到YOLOv13,用于在人机交互中强大的手势识别
Yifang Gao1, Wei Luo2, Shunshun Zhang3
1School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300, Penang, Malaysia.
Scientific reports
|November 14, 2025
概括
YOLOv9t在人机交互 (HRI) 的手势检测准确度方面表现出色,而YOLOv10n提供了最快的推断. 本研究将TSL数据集上的YOLOv8n-YOLOv13n模型进行比较.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 精确的手势检测对于直观的人机交互 (HRI) 至关重要.
- 机器人需要解释非语言线索,以便在动态环境中安全操作.
- 评估不同的YOLO (你只看一次) 模型对于静态手势识别是必要的.
研究的目的:
- 通过用于静态手势识别的YOLOv13n模型来评估YOLOv8n的性能.
- 为了比较各种YOLO模型的检测精度和计算效率.
- 为不同的HRI应用要求确定最佳模型.
主要方法:
- 利用TSL数据集,包括31个手势类的5469个灰度图像.
- 训练有素的YOLOv8n-YOLOv13n模型与一致的数据增强.
- 使用对象检测指标 (精度,回忆,mAP) 和计算效率指标 (推断速度,率,模型大小,训练时间) 评估的模型.
主要成果:
- YOLOv9t表现出卓越的检测准确性,实现了最高的mAP (0.990在IoU0.50,0.876在IoU0.50-0.95),精度 (0.975),和回忆 (0.966).
- YOLOv10n实现了0.7毫秒的最低推理延迟.
- 在评估的模型中,检测精度和计算效率之间存在明显的权衡.
结论:
- 在这项研究中,YOLOv9t是最准确的静态手势识别模型.
- YOLOv10n为延迟关键的HRI应用提供了最佳性能.
- 在YOLOv9t和YOLOv10n之间做出选择取决于HRI系统对准确性与速度的具体要求.
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