The COVID-19 epidemic has been a significant healthcare challenge in the United States. COVID-19 is transmitted predominately by respiratory droplets generated when people breathe, talk, cough, or sneeze. Wearing a mask is the primary, effective, and convenient method of blocking 80% of respiratory infections. Therefore, many face mask detection systems have been developed to supervise hospitals, airports, publication transportation, sports venues, and retail locations. However, the current commercial solutions are typically bundled with software or hardware, impeding public accessibility. In this paper, we propose an in-browser serverless edge-computing-based face mask detection solution, called Web-based efficient AI recognition of masks (WearMask), which can be deployed on common devices (e.g., cell phones, tablets, computers) with internet connections using web browsers. The serverless edge-computing design minimizes the hardware costs (e.g., specific devices or cloud computing servers). It provides a holistic edge-computing framework for integrating (1) deep learning models (YOLO), (2) high-performance neural network inference computing framework (NCNN), and (3) a stack-based virtual machine (WebAssembly). For end-users, our solution has advantages of (1) serverless edge-computing design with minimal device limitation and privacy risk, (2) installation-free deployment, (3) low computing requirements, and (4) high detection speed. Our application has been launched with public access at facemask-detection.com.
SARS-CoV-2 is a highly contagious, airborne-transmission, virus that can be spread by people who do not have obvious symptoms. In 2020, that combination of features forced much of the world to impose a wide variety of forms of social distancing, ranging from simple recommendations restricting how shared spaces can be used to rigidly enforced quarantines. It is unclear how much distancing is enough, but it is clear that the economic and emotional costs of distancing are high. Fortunately, consistent use of simple face masks dramatically reduces the probability of others becoming infected. The catch is that a significant fraction of the US population either is refusing to wear masks or is wearing masks in ways that render them ineffective. For example, it is problematic for a shop owner to prevent potential customers who are not properly masked from entering their store. Thus, we have created the Covered Safe Entry Scanner–an open source system that uses image processing methods to automatically check for proper use of masks and potentially deny entry to those who do not comply. This paper describes the design, algorithms, and performance of the mask recognition system.