Mask Classification and Head Temperature Detection Combined with Deep Learning Networks

Due to the COVID-19 pandemic, wearing a mask is mandatory in public spaces, as properly wearing a mask offers a maximum preventive effect against viral transmission. Body temperature has also become an important consideration in determining whether an individual is healthy. In this work, we design a real-time deep learning model to meet current demand to detect the mask-wearing position and head temperature of a person before he or she enters a public space. In this experiment, we use a deep learning object detection method to create a mask position and head temperature detector using a popular one-stage object detection, RetinaNet. We build two modules for the RetinaNet model to detect three categories of mask-wearing positions and the temperature of the head. We implement an RGB camera and thermal camera to generate input images and capture a person’s temperature respectively. The output of these experiments is a live video that carries accurate information about whether a person is wearing a mask properly and what his or her head temperature is. Our model is light and fast, achieving a confidence score of 81.31% for the prediction object and a prediction speed below 0. 1s/image.