Research on Abnormal Behavior Detection Algorithm for Cage Personnel Based on Machine Vision
WANG Zhe
PAN Dongya
LI Qing
FU Zhe
ZHANG Bin
ZHAN Yuheng
Abstract：The coal mine cage is the main transportation tool for miners to enter and exit the mine,undertaking the task of transporting from the ground to hundreds of meters deep underground.Due to the fast speed and limited space of the cage during operation,miners may experience serious safety accidents such as unstable standing and accidental falls inside the cage.A cage assistance system based on personnel behavior detection was proposed to enhance the safety of coal mining operations,especially to ensure the safety of coal miners underground and during transportation.The system uses IP cameras to monitor the inside of the cage in real time,and realizes target detection and behavior recognition algorithms through edge computing equipment,so as to detect and deal with abnormal behaviors of miners in a timely manner.Adopting lightweight MobileNetv2 and improved TSM model,and adding multi-scale region feature fusion module(MRFA)to improve the accuracy and robustness of behavior recognition.The experimental results showed that the improved TSM model outperformed traditional behavior recognition algorithms in terms of accuracy,parameter count,and inference speed,especially exhibiting higher efficiency and reliability on edge devices with limited computing resources.The improved TSM model achieves an accuracy of 99.30％on the miner behavior dataset,significantly higher than other mainstream behavior recognition algorithms,and has high practical value and broad application prospects.
Keywords：behavior recognitionedge computingcageabnormal alarmmine safety
Publication Date：2024-10-12
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 14-18,21 )