在施工现场使用YOLOv10和变压器架构进行非PPE自动检测,用于监控和身体佩戴摄像头,并具有基准数据集
1Institute for Environmental Design and Engineering, University College London, London, WC1H 0NN, UK. seung-hyun.wang@ucl.ac.uk.
Scientific reports
|July 27, 2025
概括
这项研究评估了YOLOv10模型,用于检测建筑工地上的个人防护设备 (PPE) 合规性. 基于Swin变压器的YOLOv10实现了最高的精度,通过自动监控提高了工人安全.
科学领域:
- 建筑工程安全 建筑工程安全
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 在建筑行业,工人的安全至关重要,个人防护设备 (PPE) 的合规性是关键因素.
- 需要自动监测个人防护设备的合规性,以提高实时应用程序的准确性和效率.
- 现有的物体检测方法在实现实际使用的高性能方面面临挑战.
研究的目的:
- 为了评估"只看一次"版本10 (YOLOv10) 的对象检测模型,用于检测必需的个人防护设备.
- 评估基于变压器的架构的性能与个人防护设备检测的传统方法相比.
- 为建筑工地安全提供先进物体检测的实际应用提供见解.
主要方法:
- 利用来自不同来源的广泛数据集,包括监控和身上的摄像头,并对基准数据集进行了比较.
- 实施和评估了各种骨干配置的YOLOv10物体检测模型.
- 专注于检测头盔,口罩,背心,手套和鞋子的不合规情况.
主要成果:
- 基于Swin变压器的YOLOv10模型在所有PPE类别中表现出卓越的性能.
- 获得高AP50分:92.4% (没有头盔),88.17% (没有口罩),87.17% (没有背心),85.36% (没有手套),83.48% (没有鞋子).
- 变压器架构在检测个人防护设备不合规方面表现优于传统方法.
结论:
- YOLOv10,特别是用变压器骨干,提供了一个高度准确和高效的解决方案,用于自动化个人防护设备合规监控.
- 这些发现支持先进人工智能的实际实施,以提高建筑工地安全.
- 未来的研究应该探索该技术的进一步优化和更广泛的应用.
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