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DAF-YOLO:在建筑工地检测不安全行为
Qi Xu1, Xiang Cheng1, Xiaoxiong Zhou1
1College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
这项研究引入了改进的YOLOv5算法,用于检测不安全的施工行为,提高了3.6%的准确性,并改善了小目标检测,以提高工作场所安全.
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:
- 建筑工地呈现复杂的环境,有许多潜在的危险.
- 工人的行为,比如不佩戴安全装备,有助于事故.
- 现有的目标检测与复杂的背景和小目标作斗争,阻碍了安全监测.
研究的目的:
- 开发一种先进的算法,用于检测建筑工地上不安全的工人行为.
- 为了提高安全应用的目标检测的准确性和稳定性.
- 加强对小目标的识别,这些小目标表明不安全的做法.
主要方法:
- 一个增强的YOLOv5算法,包括双适应功能融合模块 (FE-AFFM和BE-AFFM).
- 前端自适应特征融合模块 (FE-AFFM),用于在复杂的背景中改进特征提取.
- 后端自适应特征融合模块 (BE-AFFM) 用于加强特征融合.
主要成果:
- 与基线YOLOv5模型相比,改进的算法实现了3.6%的平均精度.
- 在检测小目标方面观察到显著的改进.
- 在定制数据集和SHWD数据集上的实验表明,与主流算法相比,性能优越.
结论:
- 拟议的双自适应特征融合算法有效地增强了在建筑工地检测不安全行为的能力.
- 该算法展示了强大的概括能力,并解决了复杂环境和小目标检测中的局限性.
- 这一进步有助于改善建筑行业的安全监测和事故预防.
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