A health crisis is raging all over the world with the rapid transmission of
the novel-coronavirus disease (Covid-19). Out of the guidelines issued by the
World Health Organisation (WHO) to protect us against Covid-19, wearing a
facemask is the most effective. Many countries have necessitated the wearing of
face masks, but monitoring a large number of people to ensure that they are
wearing masks in a crowded place is a challenging task in itself. The
novel-coronavirus disease (Covid-19) has already affected our day-to-day life
as well as world trade movements. By the end of April 2021, the world has
recorded 144,358,956 confirmed cases of novel-coronavirus disease (Covid-19)
including 3,066,113 deaths according to the world health organization (WHO).
These increasing numbers motivate automated techniques for the detection of a
facemask in real-time 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 in still 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.

利用深度学习，我们提出了一种在实时场景中检测口罩的技术，适用于通过网络摄像头记录的静止或动态的单人或多人框架，我们的模型准确率高达 99%（白天的单人）至 74%（夜晚的多人）。