摘要
不安全行为管理是煤矿安全管理工作中的重要组成部分。融合现代技术并实现煤矿井下员工不安全行为的智能识别,对煤矿安全管理的智能化建设具有重要意义。为实现井下员工不安全行为的智能识别,首先将不安全行为分为静态不安全行为、动态不安全行为和互动不安全行为。然后设计了一种基于计算机视觉的不安全行为识别系统,该系统包括视频采集、数据仓库、视频处理、模型管理、规则推理和预警系统6个子系统,分别对应井下监控视频的汇聚和管理、数据存储、静态和动态不安全行为识别、模型管理、互动不安全行为识别和预警触发、预警管理六大功能。整个识别系统采用OpenPose神经网络对人和人体关键点进行识别,采用YoloV3神经网络对设备与环境进行识别,采用MobileNetV3神经网络与ST-GCN神经网络分别对静态不安全行为和动态不安全行为进行识别,采用规则推理对互动不安全行为进行识别;神经网络模型基于Python+PyTorch编写,并采用公共数据集和专用数据集分别进行预训练和正式训练;整个系统对静态不安全行为、动态不安全行为、互动不安全行为识别的准确率分别达到了89.4%,90.7%和75.6%;最后基于Kubernetes+Docker容器的部署方式实现了识别系统在实际生产环境中的部署。整个研究融合了多种方法,实现了多种井下不安全行为的识别,并在实际生产环境中得到应用。
Unsafe behavior management is an important part of coal mine safety management.Integrating modern technologies to realize the intelligent recognition of unsafe behavior of coal mine employees is of great significance to the intelligent construction of coal mine safety management.In order to realize the intelligent recognition of underground employees’unsafe behaviors,the unsafe behaviors are divided into static unsafe behaviors,dynamic unsafe behaviors and interactive unsafe behaviors.Then,an unsafe behavior recognition system based on computer vision is designed.The system includes six subsystems:video acquisition,data warehouse,video processing,model management,rule reasoning and early warning system,which correspond to the six functions that include the aggregation and management of underground monitoring video,data storage,static and dynamic unsafe behavior recognition,model management,interactive unsafe behavior identification and early warning trigger,and early warning management.The whole system uses Open Pose neural network to identify the key points of human and human body,uses YoloV3 neural network to identify equipment and environment,uses MobileNetV3 neural network to identify static unsafe behavior,uses ST-GCN neural network to identify dynamic unsafe behavior and uses rule reasoning method to identify interactive unsafe behavior.The neural network model is written using Python+pytorch,and uses public data set and special data set for pre-training and formal training.The accuracy of static unsafe behavior,dynamic unsafe behavior and interactive unsafe behavior recognition of the whole system is up to 89.4%,90.7%and 75.6%respectively.Finally,based on kubernetes+docker container,the deployment of the identification system in the actual production environment is realized.The whole research integrates a variety of methods to the identification of multiple unsafe behaviors,and has been applied in the actual production environment.
作者
刘浩
刘海滨
孙宇
王竞陶
黄辉
LIU Hao;LIU Haibin;SUN Yu;WANG Jingtao;HUANG Hui(School of Management,China University of Mining and Technology-Beijing,Beijing 100083,China)
出处
《煤炭学报》
EI
CAS
CSCD
北大核心
2021年第S02期1159-1169,共11页
Journal of China Coal Society
基金
中央高校基本科研业务费专项资金资助项目(2009QG10)
关键词
煤矿
不安全行为
智能识别系统
神经网络
coal mine
unsafe behavior
intelligent recognition system
neural network
作者简介
刘浩(1990—),男,吉林吉林人,博士研究生。E-mail:liuhao7976@foxmail.com;通讯作者:刘海滨(1969—),男,吉林公主岭人,教授,博士生导师,博士。Tel:010-62339319,E-mail:hbliu@cumtb.edu.cn
