Research on mine worker behavior detection in low-light underground coal mine environments
DONG Fangkai
ZHAO Meiqing
HUANG Weilong
Abstract：The underground coal mine environment is complex,leading to missed and false detections when monitoring behaviors of mine workers under certain operational conditions.To address this issue,a method for detecting mine worker behaviors in low-light underground environments is proposed,which includes two parts:a low-light image enhancement and a behavior detection.The low-light image enhancement(SC1+)was improved based on self-calibrated illumination(SCI)learning,which consists ofan image enhancement network and a calibration network.The behavior detection improved the YOLOv8n model by incorporating the Dynamic Head detection,a cross-scale fusion module,and the Focal-EIoU loss function.Enhanced images from the SCI+network were used as inputs to the behavior detection model to complete the tasks of mine worker behavior detection in low-light underground environments.Experimental results showed that:①the method for mine worker behavior detection in low-light underground environments achieved an mAP@0.5 of 87.6％,representing an improvement of 2.5％over YOLOv8n,and improvements of 15.7％,11.5％,0.9％,and 4.3％compared to SSD,Faster RCNN,YOLOv5s,and RT-DETR-L,respectively.② The method had a parameter count of 3.6×106,a computational complexity of 11.6×109,and a detection speed of 95.24 frames per second.③ On the public EXDark dataset,the method achieved an mAP@0.5 of 74.7％,which was 1.5％higher than YOLOv8n,demonstrating strong generalization capability.
Keywords：low-light environmentunderground mine worker behavior detectionself-calibrated illumination learningimage enhancementSCI+networkDynamic Headcross-scale fusion moduleFocal-EIoU loss functionYOLOv8n
Publication Date：2025-01-09
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 21-30,144 )