工矿自动化2026，Vol.52Issue(5)：119-127,9.DOI:10.13272/j.issn.1671-251x.2026010084
基于改进YOLOv11的煤矿井下人员安全帽佩戴检测方法
Safety helmet wearing detection method for underground coal mine personnel based on improved YOLOv11
摘要
Abstract
Uneven illumination,diffuse dust,multiple occlusions,and complex background textures in underground coal mine environments lead to low accuracy in safety helmet wearing detection for personnel based on computer vision technology,while the limited computing power of underground explosion-proof edge computing terminals imposes high requirements on detection model size and inference efficiency.To address this problem,an improved YOLOv11 model integrating frequency-domain enhancement and efficient lightweight mechanisms was proposed.A C3k2_WTConv module based on wavelet convolution was introduced into the YOLOv11 backbone,and multiresolution analysis was used to decouple illumination noise and texture,thereby enhancing feature extraction capability.A SlimNeck feature fusion network based on the GSConv lightweight operator and VoVGSCSP topology was constructed in the neck network to reduce computational redundancy while maintaining cross-scale feature interaction.A parameter-sharing Detect_Efficient detection head was designed to improve inference efficiency.Parameter smoothing based on exponential moving average and a multi-source domain adaptation transfer learning strategy were adopted to solve the scarcity of violation samples under extreme underground working conditions,enhancing the cross-domain generalization capability and robustness of the model in unstructured environments.The improved YOLOv11 model was used for safety helmet wearing detection of underground coal mine personnel.Verification on the CUMT-Helmet and DsLMF+Helmet datasets showed that the mAP@0.5 of the model reached 97.5％,outperforming mainstream single-stage detection models YOLOv7-tiny,YOLOv8s,and YOLOv11 and improved models MH-YOLO and WAM-YOLO;the single-frame processing time was only 10.2 ms,and the model exhibited higher confidence and lower missed detection rates under extreme working conditions such as strong light interference,small-scale distant targets,and dynamic target blur.关键词
井下智能视频监控/安全帽佩戴检测/YOLOv11/小波卷积/轻量化特征融合/参数共享/迁移学习Key words
underground intelligent video surveillance/safety helmet wearing detection/YOLOv11/wavelet convolution/lightweight feature fusion/parameter sharing/transfer learning分类
矿业与冶金引用本文复制引用
景宁波,徐子骏,潘红光,秦学斌,雷心宇,米文毓,石珂珂..基于改进YOLOv11的煤矿井下人员安全帽佩戴检测方法[J].工矿自动化,2026,52(5):119-127,9.基金项目
国家自然科学基金资助项目(U24A2092) （U24A2092）
陕西省2024年自然科学基础研究计划项目(2024JC-YBMS-418) （2024JC-YBMS-418）
陕西省教育厅科学研究计划服务地方专项项目(23JC049). （23JC049）
