Real-Time Face Mask Detection to Ensure COVID-19 Precautionary Measures in the Developing Countries
Source abstract
Abstract
Recently, the rapid transmission of Coronavirus 2019 (COVID-19) is causing a significant health crisis worldwide. The World Health Organization (WHO) issued several guidelines for protection against the spreading of COVID-19. According to the WHO, the most effective preventive measure against COVID-19 is wearing a mask in public and crowded areas. It is quite difficult to manually monitor and determine people with masks and no masks. In this paper, different deep learning architectures were used for better results evaluations. After extensive experimentation, we selected a custom model having the best performance to identify whether people wear a face mask or not and allowing an easy deployment on a small device such as a Jetson Nano. The experimental evaluation is performed on the custom dataset that is developed from the website (See data collection section) after applying different masks on those images. The proposed model in comparison with other methods produced higher accuracy (99% for training accuracy and 99% for validation accuracy). Moreover, the proposed method can be deployed on resource-constrained devices.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Face recognition and analysis · Infection Control and Ventilation · COVID-19 diagnosis using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, Islamia College Peshawar, Peshawar 25120, Pakistan
Robotics and IoT Lab, Prince Sultan University, Riyadh 11586, Saudi Arabia
Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia
Department of Electrical Engineering, College of Engineering and Information Technology, Unaizah Colleges, Unaizah 56453, Saudi Arabia
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