Detecting and Counting People without Mask with Deep Neural Network

In this work, we develop a real-time computer vision system to detect people and judge whether each person is wearing a mask or not. We construct a 2-stage algorithm based on deep convolutional neural networks, where the masks are treated as objects in an image. Furthermore, in order to improve the accuracy of recognizing masks when the human face occupies a large area of the image, we adopt the dilated convolution algorithm to solve this problem. Based on the recent research of COVID-19 for infection danger, this system can send dangerous signal level 1-3 due to the proportion of masked people in the captured image. Due to the report of infection danger criteria, this system can send an alarm of three levels with the borders at 20% and 50% of the people without masks in the area, which can notify people in the area as a safe, a little dangerous, or a particularly dangerous situation.