  The COVID-19 epidemic has been a significant healthcare challenge in the
United States. According to the Centers for Disease Control and Prevention
(CDC), COVID-19 infection 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 all respiratory
infections. Therefore, many face mask detection and monitoring systems have
been developed to provide effective supervision for hospitals, airports,
publication transportation, sports venues, and retail locations. However, the
current commercial face mask detection systems are typically bundled with
specific 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 any common devices (e.g., cell phones, tablets, computers)
that have internet connections using web browsers, without installing any
software. The serverless edge-computing design minimizes the extra hardware
costs (e.g., specific devices or cloud computing servers). The contribution of
the proposed method is to provide a holistic edge-computing framework of
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 web-based 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 WearMask application has been launched with public
access at facemask-detection.com.
