基于深度学习的井下人员不安全行为识别与预警系统研究  

Research on Underground Personnel Unsafe Behavior Identification and Early Warning System Based on Deep Learning

作　　者：李雯静[1] 刘鑫[1] LI Wenjing;LIU Xin(School of Resources and Environmental Engineering,Wuhan University of Science and Technology,Wuhan 430081,China)

机构地区：[1]武汉科技大学资源与环境工程学院,湖北武汉430081

出　　处：《金属矿山》2023年第3期177-184,共8页Metal Mine

基　　金：湖北省高等学校优秀中青年科技创新团队计划项目(编号:T2020002)。

摘　　要：井下作业人员的不安全行为是矿山事故发生的主要原因之一,现有的井下监控方式仍然以人工监控为主,无法快速识别作业人员的不安全行为,导致难以实时预警。设计了一种基于深度学习的井下人员不安全行为识别与预警系统开发方案,首先制作以井下环境为背景的数据集,然后采用YOLOv4网络模型对矿工及安全帽等进行识别以判断安全帽佩戴情况,再采用OpenPose算法及ST-GCN模型对监控视频中的矿工行为进行识别,最后通过系统对不安全行为进行自动预警。结合多种深度学习和计算机开发技术开发了一种井下人员不安全行为识别与预警系统,为井下人员的安全管理提供了新的思路,对于智慧矿山建设具有参考意义。The unsafe behavior of underground operators is one of the main causes of mine accidents.The existing underground monitoring methods are still mainly manual monitoring,which can not quickly identify the unsafe behavior of operators,resulting in the difficulty of real-time early warning.A deep learning-based development scheme of unsafe behavior identification and early warning system for underground personnel is designed.Firstly,the data set based on underground environment is made,and then the YOLOv4 network model is used to identify miners and safety helmets to judge the wearing of safety helmets.Then,openpose algorithm and ST-GCN model are adopted to identify the miners′behavior in the monitoring video.Finally,the system is used to automatically warn the unsafe behavior.Combining various deep learning and computer development techniques,a kind of identification and early warning system for unsafe behavior of underground personnel is developed,which provides a new idea for safety management of underground personnel,at the same time,it has reference significance for the construction of intelligent mine.

关 键 词：智慧矿山 深度学习 ST-GCN YOLOv4 监控系统 

分 类 号：TD76[矿业工程—矿井通风与安全]