  During the COVID-19 pandemic, wearing a face mask has been known to be an
effective way to prevent the spread of COVID-19. In lots of monitoring tasks,
humans have been replaced with computers thanks to the outstanding performance
of the deep learning models. Monitoring the wearing of a face mask is another
task that can be done by deep learning models with acceptable accuracy. The
main challenge of this task is the limited amount of data because of the
quarantine. In this paper, we did an investigation on the capability of three
state-of-the-art object detection neural networks on face mask detection for
real-time applications. As mentioned, here are three models used, Single Shot
Detector (SSD), two versions of You Only Look Once (YOLO) i.e., YOLOv4-tiny,
and YOLOv4-tiny-3l from which the best was selected. In the proposed method,
according to the performance of different models, the best model that can be
suitable for use in real-world and mobile device applications in comparison to
other recent studies was the YOLOv4-tiny model, with 85.31% and 50.66 for mean
Average Precision (mAP) and Frames Per Second (FPS), respectively. These
acceptable values were achieved using two datasets with only 1531 images in
three separate classes.
