Facemask Wearing Alert System Based on Simple Architecture With Low-Computing Devices

Abstract

The Covid-19 epidemic has been causing heavy losses to humanity in terms of population, economy, and political stability. To deal with outbreaks of the pandemic, countries have been racing to develop vaccines and issue many regulations for people in daily life. Wearing a facemask in public is mandatory and will be severely punished if violated. In addition to the above mandatory regulations, it is necessary to develop tools for early warning when the human does not wear the facemask in public places such as offices, schools, supermarkets, train stations, etc. This paper proposed a facemask wearing alert system based on a simple convolutional neural network (CNN) operating on low-computing devices. This system works in two stages: face detection and facemask classification. In the first stage, it uses a face detection network with the main benefit of convolution, separable depthwise convolution, and double detectors layer to extract face region of interest (RoI). Then, this image area will go through a facemask classification network that exploits the advantages of convolution, separable depthwise convolution, and skip connection layers to classify facemask wearing (Mask or NoMask). The proposed networks are trained and evaluated on benchmark datasets. Along with simple designs, optimizing network parameters without ignoring accuracy, the system works in real-time at 33.17 and 26.18 frames per second (FPS) on an Intel Core I7-4770 CPU &#x0040; 3.40 GHz (Personal Computer - PC) and a Nvidia Maxwell GPU (Jetson Nano device), respectively. The demo video can be found here <uri>https://bit.ly/3yUgb8f</uri>.