基于YOLOv11-MCF的煤矿井下作业头盔佩戴检测方法OA

Objectives In complex underground coal mine environments characterized by uneven illumina-tion 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 convolu-tional 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 fea-ture 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 con-ducted on the CUMT HelmeT dataset.Comparative experiments determine the optimal improvement strat-egy,and ablation studies verify the superiority of the combined strategy.All models are trained for 100 ep-ochs 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.Con-clusions 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.