计算机科学 ›› 2021, Vol. 48 ›› Issue (11): 268-275.doi: 10.11896/jsjkx.200900098
金雨芳, 吴祥, 董辉, 俞立, 张文安
JIN Yu-fang, WU Xiang, DONG Hui, YU Li, ZHANG Wen-an
摘要： 安全生产管理是建筑、重工业等高危企业发展的重要方针,安全帽在施工生产环境中对人员头部防护起着关键作用,因此加强安全帽佩戴监管十分必要。近年来,基于图像视觉的安全帽佩戴监测方法成为了企业实施管理的主要手段,如何提高安全帽佩戴检测精度和检测速度是应用的关键难题。针对上述问题,文中提出了一种基于改进YOLO v4的安全帽佩戴检测算法。首先,在YOLO v4算法的3个特征图输出的基础上增加了128×128特征图输出,从而将特征图输出的8倍下采样改为4倍下采样,为后续特征融合提供了更多小目标特征。其次,基于密集连接的思想对特征融合模块进行改进以实现特征重用,使得负责小目标检测的Yolo Head分类器可以结合不同层次特征层的特征,从而得到更好的目标检测分类结果。最后,对比实验的结果表明,所提方法的平均精度高达91.17%,相比原网络检测精度提高了2.96%,检测速度基本不变,可达52.9 frame/s,从而在满足实时检测需求的同时可以得到更优的检测精度,有效实现了安全帽佩戴的高速高精度检测。
中图分类号:
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