Detection system for correctly wearing mask based on YOLO v5 algorithm

With the spread of the new crown epidemic and the increasing number of confirmed cases, wearing correct masks is an effective means of keeping the virus at bay. Using artificial intelligence technology to determine whether masks are being worn correctly is a possible solution. By training and learning from a certain amount of image data, target detection of masks can be achieved. RCNN or FAST-RCNN is widely used in the related field of research, although the method has good accuracy and robustness. However, there are some disadvantages such as low efficiency and long training time. In addition to this, most of the current work only stops detecting whether or not a mask is worn, but does not consider whether or not the mask is worn correctly. Therefore, this paper proposes a mask detection model based on the YOLO v5 algorithm, which uses a self-made training set to detect whether a pedestrian is wearing a mask and whether he or she is wearing it correctly. The experimental validation shows that the detection accuracy is higher, and the robustness is almost the same based on the original data set, while the practicality is greatly improved.