检测不安全的工作场所行为:Sec-YOLO模型与FEHA关注
Yang Liu1, Shuaixian Liu1, Jie Gao1
1School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, China.
PeerJ. Computer science
|September 24, 2025
概括
Sec-YOLO通过准确检测不安全的人类行为,如摔倒或使用电话来提高工业安全. 这种新型模型改进了现有方法,为现实应用提供了更好的特征检测.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 工业安全 工业安全 工业安全
背景情况:
- 工业环境需要对不安全的人类行为进行强有力的检测,以确保安全.
- 现有的多尺度目标检测模型在复杂的工业环境中存在局限性.
研究的目的:
- 介绍Sec-YOLO,这是一个用于在工业环境中检测不安全的人类行为的新型模型.
- 提高安全行为检测模型的准确性和适应性.
主要方法:
- 集成的感受场注意力卷积 (RFAConv) 用于特征聚焦.
- 集成可变形卷积网络v2 (DCNv2) 为了适应性.
- 使用多分支辅助特征金字塔网络 (MAFPN) 重组了架构.
- 引入了与DCNv2和MAFPN集成的功能增强混合注意力 (FEHA),以改善功能提取和融合.
主要成果:
- 在一个定制数据集上,Sec-YOLO实现了92.6%的mAP@0.5和63.6%的mAP@0.5:0.95.
- 与YOLOv8n模型相比,表现出2.0%和2.5%的改进.
- 成功检测到常见的不安全行为:摔倒,睡觉,使用电话,不戴头盔.
结论:
- Sec-YOLO在检测不安全的人类行为方面提供了卓越的性能.
- 该模型显示出用于实际工业安全应用的巨大潜力.
- 增强的功能处理和检测能力有助于改善安全结果.
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