张明媛;曹志颖;赵雪峰;杨震;

• 1:大连理工大学建设管理系
• 2:大连理工大学土木工程学院

摘要(Abstract)：

建筑工人头部伤害是造成建筑伤亡事故的重要原因。佩戴安全帽是防止建筑工人发生脑部外伤事故的有效措施,而在实际工作中工人未佩戴安全帽的不安全行为时有发生。因此,对施工现场建筑工人佩戴安全帽自动实时检测进行探究,将为深入认知和主动预防安全事故提供新的视角。然而,传统的施工现场具有安全管理水平低下、管理范围小、主要依靠安全管理人员的主观监测并且时效性差、不能全程监控等一系列问题。针对上述现状,提出了一种基于Tensorflow框架,具有高精度、快速等特性的Faster RCNN方法,实时监测工人安全帽佩戴状况。为评估模型性能,收集了6 000张图像用于模型的训练与测试,结果表明,该模型识别工人安全监测中佩戴安全帽工人的平均精度达到90. 91%,召回率达到89. 19%;识别未佩戴安全帽工人的精度达到88. 32%,召回率达到85. 08%。同时,针对工人未佩戴安全帽而进入施工现场的违规行为,通过施工现场入口处监控摄像头截取视频流图像帧,设置检验试验,验证了本方法在施工现场实际应用的有效性。

关键词(KeyWords)： 安全工程;施工管理;安全帽识别;深度学习;Faster RCNN

Abstract:

Keywords:

基金项目(Foundation): 中央高校基本科研业务费项目(DUT18JC44);; 大连市青年科技之星项目支持计划项目(2016RQ002)

作者(Authors): 张明媛;曹志颖;赵雪峰;杨震;

DOI: 10.13637/j.issn.1009-6094.2019.02.026

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