Aiming at the problems of low illumination, large change of target scale, serious occlusion between targets, difficult feature extraction of existing target detection network, poor detection effect, etc. in complex mine environment, an improved S3-YOLOv5s mine personnel protection equipment detection algorithm is proposed. A simple, parameter free attention module (SimAM) was added to the backbone network to improve the feature extraction capability of the network. Scale equalizing pyramid convolution (SEPC) was introduced to strengthen multi-scale feature fusion. Finally, SIoU was used as the frame regression loss function and K-means++ algorithm was used for prior anchor frame clustering to improve the frame detection accuracy. The experimental results show that, compared with the existing YOLOv5s algorithm, the average detection accuracy of the proposed algorithm in all categories is improved from 89.64% to 92.86%, and the algorithm has excellent detection capability for personnel protection equipment under complex mine environments, which verifies the effectiveness of the proposed method.