  After the outbreak of COVID-19, mask detection, as the most convenient and
effective means of prevention, plays a crucial role in epidemic prevention and
control. An excellent automatic real-time mask detection system can reduce a
lot of work pressure for relevant staff. However, by analyzing the existing
mask detection approaches, we find that they are mostly resource-intensive and
do not achieve a good balance between speed and accuracy. And there is no
perfect face mask dataset at present. In this paper, we propose a new
architecture for mask detection. Our system uses SSD as the mask locator and
classifier, and further replaces VGG-16 with MobileNetV2 to extract the
features of the image and reduce a lot of parameters. Therefore, our system can
be deployed on embedded devices. Transfer learning methods are used to transfer
pre-trained models from other domains to our model. Data enhancement methods in
our system such as MixUp effectively prevent overfitting. It also effectively
reduces the dependence on large-scale datasets. By doing experiments in
practical scenarios, the results demonstrate that our system performed well in
real-time mask detection.
