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HDS-YOLOv5:基于YOLOv5s的改进的安全带探测算法
Mingju Chen1,2, Zhongxiao Lan3, Zhengxu Duan3
1Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science & Engineering, Yibin 644002, China.
这项研究介绍了HDS-YOLOv5,这是一个改进的深度学习模型,用于在电源维护期间检测安全带的状态. 它提高了准确性和实时性能,减少了安全隐患.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 工业安全 工业安全 工业安全
背景情况:
- 不当使用安全带是电力维护中的一个重大安全隐患.
- 传统的机器视觉方法缺乏对头状态检测的准确性和实时有效性.
研究的目的:
- 开发一个新的深度学习网络,HDS-YOLOv5,用于准确和实时检测安全带的状态.
- 为了减少复杂的电力维护环境中的安全事故.
主要方法:
- 修改了YOLOv5s架构,包括一个HOOK-SPPF功能提取模块.
- 实现了脱头模块,以改善分类和回归.
- 使用Scylla 交叉点对联盟 (SIoU) 来优化损失函数.
主要成果:
- HDS-YOLOv5算法在平均平均精度 (mAP@0.5) 中实现了3%的增加,达到91.2%.
- 演示了每秒24.0 (FPS) 的检测率.
- 与现有车型相比,它表现出了更高的性能.
结论:
- HDS-YOLOv5为安全带提供了增强的特征提取和识别精度.
- 改进的模型有效地解决了复杂环境中传统方法的局限性.
- 该算法通过可靠的实时监控,有助于减少安全事故.
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