  2020 has been a year marked by the COVID-19 pandemic. This event has caused
disruptions to many aspects of normal life. An important aspect in reducing the
impact of the pandemic is to control its spread. Studies have shown that one
effective method in reducing the transmission of COVID-19 is to wear masks.
Strict mask-wearing policies have been met with not only public sensation but
also practical difficulty. We cannot hope to manually check if everyone on a
street is wearing a mask properly. Existing technology to help automate mask
checking uses deep learning models on real-time surveillance camera footages.
The current dominant method to perform real-time mask detection uses Mask-RCNN
with ResNet as the backbone. While giving good detection results, this method
is computationally intensive and its efficiency in real-time face mask
detection is not ideal. Our research proposes a new approach to mask detection
by replacing Mask-R-CNN with a more efficient model "YOLO" to increase the
processing speed of real-time mask detection and not compromise on accuracy.
Besides, given the small volume as well as extreme imbalance of the mask
detection datasets, we adopt a latest progress made in few-shot visual
classification, simple CNAPs, to improve the classification performance.
