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YOLOv5s-gnConv:检测高处工人的个人防护设备
Huihua Chen1, Yaoyu Li1, Huanxi Wen1
1School of Civil Engineering, Central South University, Changsha, Hunan Province, China.
Frontiers in public health
|October 16, 2023
概括
本研究介绍了YOLOv5s-gnconv,这是一个改进的AI模型,用于检测建筑工人个人防护设备 (PPE) 合规性. 这种先进的模型通过准确识别高处没有佩戴必要安全装备的工人来提高安全性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 职业安全 在职业安全.
背景情况:
- 从高处落 (FFH) 在建筑中构成重大风险.
- 手动检查个人防护设备 (PPE) 往往是低效和容易出错的.
- 自动检测个人防护设备对于提高工人安全至关重要.
研究的目的:
- 开发一个先进的AI模型,用于准确检测高处工人的个人防护设备合规性.
- 改进现有的对象检测模型,以适应现实建筑工地条件.
- 加强高风险工作环境的安全监测和管理.
主要方法:
- 使用Python爬行和数据增强创建了一个由10,000张高处工人的图像组成的数据集.
- 开发了三个改进的YOLOv5s模型,其中YOLOv5s-gnconv使用封闭卷积 (gnConv) 和循环设计.
- 数据集被分为培训,验证和测试集,比例为7:2:1.
主要成果:
- 这款YOLOv5s-gnconv型号在标准YOLOv5s型号上表现出卓越的性能.
- 改进包括精度增加5.01%,回忆增加4.72%,mAP_0.5.5.4.26%的提升.
- 该模型有效检测个人防护设备的合规性,有助于提高工人安全.
结论:
- YOLOv5s-gnconv模型为建筑业的自动化个人防护设备检测提供了一个强大的解决方案.
- 该研究强调了人工智能在减轻与高空落相关的风险方面的潜力.
- 其他考虑因素包括模型在不同的环境条件下 (如照明和摄像头角度) 的性能.
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