Fast detection of face masks in public places using QARepVGG-YOLOv7

The COVID-19 pandemic has resulted in substantial global losses. In the post-epidemic era, public health needs still advocate the correct use of medical masks in confined spaces such as hospitals and indoors. This can effectively block the spread of infectious diseases through droplets, protect personal and public health, and improve the environmental sustainability and social resilience of cities. Therefore, detecting the correct wearing of masks is crucial. This study proposes an innovative three-class mask detection model based on the QARepVGG-YOLOv7 algorithm. The model replaces the convolution module in the backbone network with the QARepVGG module and uses the quantitative friendly structure and re-parameterization characteristics of the QARepVGG module to achieve high-precision and high-efficiency target detection. To validate the effectiveness of our proposed method, we created a mask dataset of 5095 pictures, including three categories: correct use of masks, incorrect use of masks, and individuals who do not wear masks. We also employed data augmentation techniques to further balance the dataset categories. We tested YOLOv5s, YOLOv6, YOLOv7, and YOLOv8s models on self-made datasets. The results show that the QARepVGG-YOLOv7 model has the best accuracy compared with the most advanced YOLO model. Our model achieves a significantly improved mAP value of 0.946 and a faster fps of 263.2, which is 90.8 fps higher than the YOLOv7 model and a 0.5% increase in map value over the YOLOv7 model. It is a high-precision and high-efficiency mask detection model.