摘要
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.
作者
张萌
韩豫
刘泽锋
ZHANG Meng;HAN Yu;LIU Zefeng(Faculty of Civil Engineering and Mechanics,Jiangsu University,Zhenjiang Jiangsu 212013,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2022年第5期140-146,共7页
China Safety Science Journal
基金
国家自然科学基金面上项目资助(72071097)
教育部人文社会科学研究规划基金资助(20YJAZH034)
江苏省第十六批“六大人才高峰”高层次人才项目(SZCY-014)
2020年江苏省研究生实践创新计划项目(SJCX20_1430)。
关键词
深度学习
建筑工人
高空作业
安全防护装备
动态视频检测
deep learning
construction worker
working at height
safety protection equipment
dynamic video detection
