Real-time Face Mask-Wearing Detection and Temperature Measurement based on a Deep Learning Model

Abstract

In the context of the COVID-19 outbreak in a global scenario, mandatory mask-wearing and temperature control can effectively prevent its spread and realize self-protection. Therefore, real-time face-mask wearing and temperature measurement technology is of greater importance against the background of infectious disease prevention and control. The present study adopted MobileNet as the backbone of the single-stage RetinaFace framework for real-time face detection and mask-wearing detection. Moreover, the focal loss function of α dynamic value was adopted to avoid the class imbalance problem and improve the classification accuracy in the training stage. Regarding face temperature measurement technology, non-contact and uncooled temperature-sensitive elements were used for temperature measurement, but it was easily affected by environmental variables. Therefore, an SVR model was employed for temperature calibration with the constant temperature blackbody as reference. The alignment errors for the accuracy of face detection, mask wearing detection and temperature correction were 89.58%, 97.84% and 4.85%, respectively. The parameter quantity of the face mask wearing detection model reached 0.42 M, while the computation quantity arrived at 2.039 GFLOPs. The detection model proposed in this study combines real-time mask-wearing detection with face temperature measurement, which can help to quickly measure the body temperature and detect whether one wears face masks properly in the context of COVID-19, so as to reduce the risk of epidemic spread.