深度学习下建筑工人高空安全防护装备检测方法

Detection method of high-altitude safety protective equipment for construction workers based on deep learning

为解决现有安全防护装备检测技术动态性、协同性不足的问题,基于深度学习,提出一种建筑工人高空作业安全防护装备的检测新方法;该方法以轻量化网络MobileNetV2置换YOLOv4的主干特征提取网络,实现动态视频状态下安全帽与安全带的综合检测;并开展测试,检验该方法的有效性。结果表明:该检测方法在中央处理器(CPU)运行环境下,检测速度增快2.7倍;在图形处理器(GPU)运行环境下,对于单目标、多目标和小目标的单帧视频检测速度能保持在25~27 ms,同时能取得91.57%、89.69%和86.63%的平均精度。

In order to address the lack of dynamics and coordination in current detection technology of safety protection equipment,a new detection method for construction workers working at height was proposed based on deep learning.Comprehensive detection of helmet and seat belt in state of dynamic videos was realized by replacing backbone feature extraction network of YOLOv4 with lightweight network MobileNetV2.Then,tests were carried out to verify effectiveness of the method.The results show that detection speed of the proposed method is increased by 2.7 times in central processing unit(CPU)operating environment,and single-frame video detection speed for single target,multi-target and small target can be maintained between 25-27 milliseconds in graphics processing unit(GPU)operating environment.At the same time,mean average precision rate of 91.57%,89.69%and 86.63%can be achieved.