WANG Y W, WANG Y, LYU D H, et al. Helmet wearing detection method for underground coal mine operations based on YOLOv11-MCF[J]. Journal of Henan Polytechnic University (Natural Science) ,2026,45(3):1-9.

doi:10.16186/j.cnki.1673-9787.2025080010

Received:2025/08/08

Revised:2025/10/28

Published:2026/04/28

Helmet wearing detection method for underground coal mine operations based on YOLOv11-MCF

Wang Yanwei1, Wang Yu1, Lyu Donghan2, Jin Ge2, Wang Hao3, Yang Chensheng1

1.School of Mechanical Engineering， Heilongjiang University of Science and Technology， Harbin  150022， Heilongjiang， China；2.Early Warning Center， China Academy of Safety Science and Technology， Beijing  100012， China；3.China Coal Research Institute Co.， Ltd.， Research Institute of Mine Artificial Intelligence， Beijing  100013， China

Abstract: Objectives In complex underground coal mine environments characterized by uneven illumination and dust interference， existing helmet detection methods suffer from high miss detection and false alarm rates. For this， A helmet wearing detection method for underground coal mine operations based on YOLOv11 MCF （YOLOv11 with multi scale convolutional block， convolutional block attention module， and more focused intersection over union） is proposed.  Methods First， the MSCB （multi scale convolutional block） module is combined with C3k2， integrating depthwise separable convolution with multi scale convolution to capture multi scale features， while residual connections and channel shuffling enhance feature propagation and interaction. Second， the convolutional block attention module （CBAM） is introduced before the detection head to improve the model’s ability to extract helmet features and suppress background interference. Finally， to address the class imbalance problem， the Focaler IoU （more focused intersection over union） loss function is adopted， which enhances the model’s attention to hard to distinguish samples under dim lighting， better handles the imbalance between positive and negative samples， and significantly improves bounding box regression accuracy， further boosting detection precision. Experiments are conducted on the CUMT HelmeT dataset. Comparative experiments determine the optimal improvement strategy， and ablation studies verify the superiority of the combined strategy. All models are trained for 100 epochs under identical training parameters and hardware conditions.  Results Experimental results show that the YOLOv11 MCF algorithm achieves a helmet detection accuracy of 86.7%， which is 1.8% and 4.2% higher than that of YOLOv11 and YOLOv13， respectively， while also meeting real time requirements.  Conclusions The improved YOLOv11 MCF model can significantly enhance the detection capability for safety helmets， providing a new solution for safety helmet detection of coal mine workers.

Key words:coal mine safety;object detection;convolutional block attention module;multi-scale convolutional block;helmet