The lack of lighting and the complex environment in the mine, coupled with the small target size of safety helmets, lead to poor detection performance of safety helmets by general object detection models. To solve these issues, an improved mine safety helmet wearing detection model based on YOLOv8s is proposed. Firstly, the effectiveSE module is combined with the C2f module in the neck network of YOLOv8s to design a new C2f-eSE module, improving the feature extraction ability of the network structure. The CIoU loss function is replaced by the Wise-EIoU loss function to improve the model’s robustness. In addition, the spatial and channel reconstruction convolution (SCConv) module is introduced into the detection head. A new lightweight SPS detection head is designed based on the parameter sharing concept, reducing the number of parameters and computational complexity of the model. Finally, adding a P2 detection layer to the model enables the feature extraction network to incorporate more shallow information and improves the detection ability for small-sized targets. Experimental results show that the mAP50 index of the improved model increases by 3.2%, the number of parameters decreases by 1.6%, and GFLOPs decreases by 5.6%.