工矿自动化2025，Vol.51Issue(3)：138-147,10.DOI:10.13272/j.issn.1671-251x.2025030011
基于深度学习的煤矿井下人员不安全行为检测与识别
Detection and recognition of unsafe behaviors of underground coal miners based on deep learning
摘要
Abstract
To address challenges such as multi-scale variations in underground targets,occlusion of moving objects,and the excessive similarity between targets and the environment,a deep learning-based method was proposed for detecting and recognizing unsafe behaviours of underground coal miners.A top-down approach was adopted to construct a YOLOv5s_swin target detection model based on a self-attention mechanism.This model was developed by introducing a sliding window operation into the Transformer-based self-attention mechanism to obtain Swin-Transformer,which was then used to enhance the traditional YOLOv5s model,resulting in YOLOv5s_swin.To tackle the issue of multi-scale variations in human detection bounding boxes caused by the varying distances between underground personnel and surveillance cameras,a high-resolution feature extraction network was employed to extract human keypoints after detecting personnel.Subsequently,a spatiotemporal graph convolutional network(ST-GCN)was utilized for behaviour recognition.Experimental results showed that YOLOv5s_swin achieved an accuracy of 98.9％,an improvement of 1.5％over YOLOv5s,with an inference speed of 102 frames per second(fps),meeting real-time detection requirements.The high-resolution feature extraction network effectively extracted human keypoints at different scales,and the HRNet_w48 network,with more feature channels,outperformed HRNet_w32.Under complex industrial and mining conditions,the ST-GCN model demonstrated high accuracy and recall rates,enabling precise classification of miners'behaviors,with an inference speed of 31 fps,thereby meeting underground monitoring requirements.关键词
井下不安全行为识别/目标检测/深度学习/自注意力机制/YOLOv5s/高分辨率特征提取网络/时空图卷积网络Key words
underground unsafe behaviour recognition/object detection/deep learning/self-attention mechanism/YOLOv5s/high-resolution feature extraction network/spatiotemporal graph convolutional network分类
矿业与冶金引用本文复制引用
郭孝园,朱美强,田军,朱贝贝..基于深度学习的煤矿井下人员不安全行为检测与识别[J].工矿自动化,2025,51(3):138-147,10.基金项目
国家自然科学基金项目(62373360). （62373360）
