基于AlphaPose的井下不安全行为监测方法

A Monitoring Method of Unsafe Behavior under the Shaft Based on AlphaPose

人为因素是煤矿事故的最主要原因,预防和制止煤矿井下人员的不安全行为是降低事故率的根本途径。针对违规进入危险区域、吸烟行为、未佩戴安全帽三种主要不安全行为,提出了一种结合AlphaPose和YOLOv5的不安全行为监测方法,实现公共模块复用,并建立不安全行为判定算法。经过实验对比分析,对上述三种不安全行为监测的准确率分别达到92.33%、92.76%、95.12%,监测速率为11.91 f/s,均优于对比算法,算法具有较高的准确率和实时性。

Human factors are the most main causes of coal mine accidents.Preventing and stopping the unsafe behavior of personnel under the coal mine shaft is the fundamental way to reduce the accident rate.Aiming at the three main unsafe behaviors of entering dangerous areas illegally,smoking behavior and not wearing a helmet,an unsafe behavior monitoring method combined with AlphaPose and YOLOv5 is proposed.It realizes common module reuse and establishes unsafe behavior judgment algorithms.After comparative experimental analysis,the accuracy rates of the above three unsafe behavior monitoring are 92.33%,92.76% and 95.12%,respectively,and the monitoring rate is 11.91 f/s,which are all better than the comparison algorithms.The algorithm has higher accuracy and real-time performance.