基于改进的YOLOv5的头盔佩戴检测算法
Yiping Liu1, Benchi Jiang2, Huan He3
1School of Mechanical Engineering, Anhui Polytechnic University, Wuhu, 241000, People's Republic of China.
Scientific reports
|April 16, 2024
概括
这项研究使用改进的YOLOv5算法在工业环境中增强了头盔检测. 新型号提供了更高的准确性和更快的实时检测,这对工人的安全至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 工业安全 工业安全 工业安全
背景情况:
- 准确的头盔检测对于工业环境中的工人安全至关重要.
- 现有的方法难以应对不同的照明,视角和遮蔽,限制了检测精度.
研究的目的:
- 开发一个实时头盔检测系统,提高准确性和效率.
- 在具有挑战性的工业条件下解决当前目标检测算法的局限性.
主要方法:
- 通过整合FasterNet轻量级网络结构,改进了YOLOv5算法.
- 实现了具有动态聚焦机制的Wise-IoU损失函数.
- 引入了CBAM关注机制,以加强全球背景和小目标检测.
主要成果:
- 模型参数减少了12.68%,计算负载减少了10.8%.
- 平均精度 (mAP) 从88.3%提高到92.3%.
- 推断时间减少了81.5%,从而实现了有效的实时检测.
结论:
- 增强的YOLOv5模型显著提高了头盔检测性能.
- 这些修改使实时工业安全监测系统更有效,更准确.
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