Comparative Analysis of Convolutional Neural Network Architectures for Real Time COVID-19 Facial Mask Detection

Abstract

The late 2019 outbreak of Coronavirus Disease (COVID-19) had an indelible imprint on the humanity. The world is recovering from the outbreak but there is danger of a second wave of the outbreak. To get rid of the outbreak it is necessary to prevent the viral transmission and it is need of the hour to maintain social distancing and wear masks in public areas. The governments are providing strict guidelines to wear masks in public places. It is not manually feasible to check if people are wearing masks or not. In this paper, process of detecting face masks in public places is automated using Convolutional Neural Networks by performing comparative analysis on Sequential bi-layered CNN, VGG-16 CNN and MobileNetV2 CNN architectures. Among these three architectures MobileNetV2 outperformed with a performance accuracy of 99.2%. The efficient Deep Learning architecture of detecting face masks can be achieved with the help of IoT (Internet of Things) devices and cameras, of those who are not following guidelines in public places. Such a system is very useful in post outbreak period and can be installed in public places such as Railway Stations, Airports, Parks, Schools, colleges, offices etc. to track and ensure wearing of masks by people. The contribution of this paper is not to reel-off the finding from the original paper on Face Mask detection with various architectures rather to provide results on the efficiency of using the MobileNetV2 architecture in comparison with Sequential CNN and VGG-16 architectures for crowd analysis mask detection.