煤矿安全2024，Vol.55Issue(11)：227-233,7.DOI:10.13347/j.cnki.mkaq.20231760
基于全景可视化的综采工作面异常行为识别关键技术
Key technologies for identifying abnormal behaviors in fully mechanized mining faces based on panoramic visualization
杨梁 1王鹏 1李立杰 1王唯德1
作者信息
- 1. 陕西银河煤业开发有限公司,陕西 榆林 719000
- 折叠
摘要
Abstract
A lightweight human pose recognition model based on OpenPose improvement is proposed to address the difficulty of in-telligent supervision of abnormal behaviors of coal mine workers.The backbone network of original model is replaced with Mobile-Net-v3,and the original 7×7 convolutional kernels is replaced with a smaller convolutional kernel to improve model detection speed and reduce resource consumption.In addition,due to the limited shooting range of the working face camera,it is difficult to meet the needs of panoramic visualization of the working face.This article adopts the SIFT image stitching algorithm to fuse multiple monit-oring perspectives and achieve real-time synchronization of full scene monitoring of the working face.After algorithm experiments,the improved human posture recognition model has an accuracy of 81.5%,occupies 42%of the memory of the original model,and the detection speed is 1.59 times that of the original model.The improved human posture recognition model not only reduces the memory consumption,but also greatly improves the speed of model detection and maintains a high accuracy.Combining human pos-ture recognition and image stitching algorithm can realize real-time monitoring of workers’behaviors.关键词
煤矿工作面视频监测/人体姿态估计/图像拼接/OpenPose/SIFT特征Key words
video monitoring of coal mine working face/human pose estimation/image stitching/OpenPose/SIFT features分类
矿业与冶金引用本文复制引用
杨梁,王鹏,李立杰,王唯德..基于全景可视化的综采工作面异常行为识别关键技术[J].煤矿安全,2024,55(11):227-233,7.
