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使用改进的YOLOv5s检测安全头盔和口罩的使用情况
Shuangyuan Li1, Yanchang Lv2, Xiangyang Liu2
1Information Construction Office, Jilin Institute of Chemical Technology, Jilin City, 132022, China. lsy@jlict.edu.cn.
Scientific reports
|December 4, 2023
概括
这项研究引入了一种轻量级的AI方法,用于市政建筑安全,同时检测口罩和头盔. 这种方法提高了精度,同时大大降低了计算负载,以提高安全合规性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 土木工程 土木工程是指土木工程.
背景情况:
- 确保市政建设人员安全至关重要,尤其是在疫情防控措施下.
- 当前的安全监控方法在复杂的环境中可能缺乏效率和精度.
研究的目的:
- 开发一种轻量级的人工智能 (AI) 检测方法,同时识别在市政建筑中佩戴口罩和安全头盔的个人.
- 通过先进的深度学习技术,提高安全监测的精度和效率.
主要方法:
- 使用了YOLOv5s网络框架与ShuffleNetv2功能提取机制,以减少计算开销.
- 整合了ECA注意力机制和优化损失功能,以提高功能地图质量和检测准确性.
- 开发了一种轻量级的AI模型,用于同时检测口罩和安全头盔.
主要成果:
- 在平均平均精度 (mAP) 中实现了4.3%的改进.
- 分别减少了54.8%和53.8%的参数和计算负载.
- 在轻量化操作和高检测精度之间取得了平衡.
结论:
- 拟议的轻量级AI检测方法有效地提高了市政建筑的安全监测.
- 这种方法为建筑安全的轻量级目标检测研究提供了有价值的参考.
- 该研究成功地平衡了计算效率与在识别关键安全设备和个人防护设备 (PPE) 中更高的准确性.
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