During the outbreak of COVID-19, while bringing various serious threats to the world, it reminds us that we need take precautions to control the transmission of the virus The rise of Internet of Medical Things (IoMT) has made related data collection and processing, including healthcare monitoring systems, more convenient on the one hand, and requirements of public health prevention are also changing and more challengeable on the other hand One of the most effective non-pharmaceutical medical intervention measures is mask-wearing Therefore, there is an urgent need for an automatic real-time mask detection method to help prevent the public epidemic In this paper, we put forward an edge computing-based mask identification framework (ECMask) to help public health precautions, which can ensure real-time performance on the low-power camera devices of buses Our ECMask consists of three main stages: video restoration, face detection, and mask identification The related models are trained and evaluated on our Bus Drive Monitoring Dataset and public dataset We construct extensive experiments to validate the good performance based on real video data, in consideration of detection accuracy and execution time efficiency of the whole video analysis, which have valuable application in COVID-19 prevention IEEE
Real-time Mask Identification for COVID-19: An Edge Computing-based Deep Learning Framework