A Real-Time Unmasked Detection Using SSD-MobileNetV2 on Edge Device for the COVID-19 Pandemic

Abstract

COVID-19 Pandemic affects daily life and the global economy. The COVID-19 virus can be spread by small liquid particles, which can be filtered using a face mask. Wearing masks in public areas is an excellent approach to preventing illness. As a result, mask detection is necessary to stop the spread of the disease before a person enters the facility. Regarding Single Shot Multibox Detector-MobileNetV2 (SSD-MobileNetV2) was used in this research to build tools to detect and monitor unmasked people in the facility or working rooms that consist of many people. In this paper, we showed the experimental performance of SSDMobileNetv2 based on an application that runs on an edge device to detect unmasked people in the room and compromise with very high accuracy of 97% in rooms smaller than 16 square meters, which is sufficient to detect the wearing of masks in public places or various locations.