Detection of a facemask in realtime using deep learning methods

Abstract

A health crisis is raging all over the world with the rapid transmission of the novel coronavirus disease COVID-19. Among of the guidelines issued by the World Health Organization (WHO) to protect against COVID-19, wearing a facemask is the most effective. Many countries required the wearing of facemasks, but monitoring a large number of people to ensure that they are wearing masks in a crowded place is a challenging task. COVID-19 quickly affected our day-to-day life as well as world trade movements. By the end of April 2021, the world had recorded 144,358,956 confirmed cases of COVID-19, including 3,066,113 deaths, according to the WHO. These increasing numbers motivated automated techniques for the detection of a facemask in realtime scenarios for the prevention of COVID-19. We propose a technique using deep learning that works for single and multiple people in a frame recorded via webcam as a still image or in motion. We have also experimented with our approach in night light. The accuracy of our model is good compared to the other approaches in the literature, ranging from 74% for multiple people in a nightlight to 99% for a single person in daylight.