Under normalized epidemic prevention and control, mask-wearing detection can promptly remind people to wear masks correctly, thus reducing the risk of cross-infection of people in public places. Aiming at the difficulty of detecting obscured and small targets in the mask-wearing detection task, a YOLO-Mask algorithm is proposed. The proposed algorithm is based on YOLOv3. It introduces an attention mechanism into the feature extraction network to enhance the model's ability to express salient features. Moreover, it uses feature pyramid and path aggregation strategies for feature fusion to enhance detailed feature information and utilize different levels of feature information. The loss function is optimized. The experimental results show that the average accuracy of the YOLO-Mask algorithm is 93.33% for mask-wearing detection targets in different scenarios, which is 7.62% higher than that of the existing YOLOv3 algorithm. The proposed algorithm has better detection results and robustness compared with other mainstream algorithms.