Detection and recognition of unsafe behaviors of underground coal miners based on deep learning
GUO Xiaoyuan
ZHU Meiqiang
TIAN Jun
ZHU Beibei
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.
Keywords：underground unsafe behaviour recognitionobject detectiondeep learningself-attention mechanismYOLOv5shigh-resolution feature extraction networkspatiotemporal graph convolutional network
Publication Date：2025-03-31
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 138-147 )