YOLO-DCRCF:一种用于检测在电网操作环境中佩戴安全头盔和手套的算法
Jinwei Zhao1, Zhi Yang1,2,3, Baogang Li1,2,3
1Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China.
Journal of imaging
|September 26, 2025
概括
本研究介绍了YOLO-DCRCF,这是一个先进的算法,用于在电网运行中准确检测安全头盔和手套. 新方法改进了现有的技术,用于加强工人安全监测.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 职业安全 在职业安全.
背景情况:
- 在电网运行中,安全头盔和手套等个人防护设备 (PPE) 是至关重要的.
- 传统的个人防护设备检测方法往往是不准确的,因为环境挑战,如照明不良,遮蔽,和密集的劳动力.
研究的目的:
- 开发一种新的算法,YOLO-DCRCF,以提高在电网环境中检测安全头盔和手套使用的准确性和可靠性.
- 在复杂的操作环境中解决现有检测方法的局限性.
主要方法:
- 提出了YOLO-DCRCF算法,这是YOLOv11的增强,集成可变形卷积网络版本2 (DCNv2) 以更好的几何转换建模.
- 引入了重新校准特征金字塔 (RCF) 网络,以改善特征交互和多层次信息捕获.
主要成果:
- 在安全头盔佩戴数据集 (SHWD) 上获得了92.7%的50% IoU (mAP50) 的平均平均精度.
- 在安全头盔和手套佩戴数据集 (SHAGWD) 上获得79.6%的mAP50评分.
- 在SHWD上表现1.1%高于基线YOLOv11模型,在SHAGWD上表现2.7%.
结论:
- 在电网环境中,YOLO-DCRCF模型在检测安全头盔和手套方面表现出卓越的性能.
- 拟议的算法符合电网运营的实时安全监控要求,提高了工人的安全.
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