工矿自动化2025，Vol.51Issue(1)：21-30,144,11.DOI:10.13272/j.issn.1671-251x.2024090032
煤矿井下暗光环境人员行为检测研究
Research on mine worker behavior detection in low-light underground coal mine environments
摘要
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.关键词
暗光环境/井下人员行为检测/自校准光照学习/图像增强/SCI+网络/Dynamic Head/跨尺度融合模块/Focal-EIoU损失函数/YOLOv8nKey words
low-light environment/underground mine worker behavior detection/self-calibrated illumination learning/image enhancement/SCI+network/Dynamic Head/cross-scale fusion module/Focal-EIoU loss function/YOLOv8n分类
矿业与冶金引用本文复制引用
董芳凯,赵美卿,黄伟龙..煤矿井下暗光环境人员行为检测研究[J].工矿自动化,2025,51(1):21-30,144,11.基金项目
山西省教育厅2022年度高等学校科技创新项目(2022L704) （2022L704）
阳泉市科技计划项目(2022JH051). （2022JH051）
