Due to the complex underground environment and the small object of the helmet, the detection accuracy is low when the original YOLOv7 algorithm is used to detect whether the mine personnel wears the helmet, which cannot be applied to the actual operation site. In response to this problem, we proposed an FM-YOLOv7 mine personnel helmet detection. First, to improve the feature extraction ability of the shallow network and enhance the representation ability of the model on the helmet, we propose the fused-MBCA (fused-MBConv with the coordinate attention) module. Second, to improve the detection ability of small objects, enable the fused features to obtain high-level semantic information and low-level details from different scales, and have more extensive receptive fields, we propose the multi-scale feature fusion efficient layer aggregation networks. Finally, to accelerate the convergence of the model and improve the regression accuracy, we use efficient intersection over union as the bounding box regression loss function. These experiments are based on the self-built mine personnel safety helmet dataset. The results show that the FM-YOLOv7 model outperformed the other six algorithms. The mAP@0.5 of the proposed model can reach 85.7%, which is 1.4% higher than the original YOLOv7 model. Also, the improved YOLOv7 model achieves 91 frames per second in detection speed, which detects whether the mine personnel wears a safety helmet in real time.