Alleviating the Risk of COVID-19: A Social Healthcare Face Mask Detection System Based on Deep Learning Techniques

Abstract

The purpose of a facemask detection-system is to ascertain in real-time whether or not an individual is wearing a face mask using an automated process. It commonly entails the utilization of Machine-Learning (ML), Deep-Learning (DL), and Computer-Vision (CV) approaches to examine images or video streams collected by cameras. The ability of the technology to distinguish between those wearing face masks and those who do not helps in the implementation of mask-wearing laws in a variety of settings, such as schools, hospitals, airports, and public transportation. One tactic being suggested to contain the spread is the wearing of face masks by persons in public areas. As a result, computerized face detection methods that are both successful and efficient are essential for such requirements. This article aims to design and implement an intelligent system that can detect faces that have been masked to mitigate the risk of COVID-19. MobileNetV2 makes integrating the system into devices with limited processing power easy. The photos are divided into two groups by this model: "with mask" and "without mask." During the model's development, it is trained and evaluated using a dataset of around 6,369 photos. The pre-trained MobileNetV2 model tha t we employed for this research achieved a 98.20% accuracy rate in terms of performance. Compared to VGG-16 and Inception-V3, the proposed system outperforms them in terms of its computational efficiency and accuracy. This work can be used as a digital verification tool in hospitals, colleges, banks, airports, and other public areas. This system can potentially improve public safety efforts and aid in preventing the spread of infectious diseases.