Optimizing YOLOv5 algorithm for Mask-wearing Detection

The novel coronavirus has a strong ability to spread and survive. Wearing a mask correctly can effectively reduce the spread of the virus among the crowd. How to intelligently and efficiently detect the wearing of a mask is of great significance. Detecting whether to wear a mask is the target detection content that many researchers are currently studying. YOLOv5 (You Only Look Once) is an excellent algorithm in target detection. Given that detecting whether a mask is worn is different from other target detection tasks, in this paper, we tried to optimize YOLOv5 algorithm to make it more suitable for mask-wearing detection. In words, detection layers, attention mechanism were added, and proper loss function was chosen strictly to the YOLOv5 target detection algorithm. So that optimal YOLOv5 algorithm model was proposed. The accuracy rate (precision), recall rate (recall) and average precision (mAP) of the algorithm on the test set were 83%, 83.3% and 81.7% respectively, higher than YOLOv3, YOLOv4, YOLOv5 detection algorithm.