基于YOLO的智能识别系统,用于在建筑工地发现隐藏的危险
Hang Li1, Peijian Jin1,2, Long Zhan3
1School of Emergency Science and Engineering, Jilin Jianzhu University, Changchun, Jilin, China.
PloS one
|September 17, 2025
概括
本研究引入了改进的YOLOv5n危险识别系统用于建筑工地,通过轻量化设计和高准确度检测不安全的行为和条件来提高安全性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 建筑工程安全 建筑工程安全
背景情况:
- 建筑行业面临着高事故率.
- 现有的危险识别系统可能缺乏效率或准确性.
研究的目的:
- 为建筑工地开发一个优化,轻量级的危险识别系统.
- 改进对不安全的人类行为和不安全物体条件的检测.
主要方法:
- 整合了ECA注意力,幽灵模块,SIoU损失和EIoU-NMS到YOLOv5n.
- 训练了两个超小型模型 (约. 2.5 MB) 在一个自定义数据集上.
- 在Jetson Nano B01边缘平台上部署了系统.
主要成果:
- 达到高平均平均精度 (mAP@0.5) 评分:93.6%对不安全的人类行为和99.5%对不安全的物体条件.
- 证明了轻量级加速和改进的精度.
- 经过验证的高效现场危险检测能力.
结论:
- 拟议的基于YOLOv5n的系统有效地提高了建筑工地安全.
- 优化的模型为实时,基于边缘的危险检测提供了一个实用的解决方案.
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