A convolutional neural network for face mask detection in IoT-based smart healthcare systems

Bose S; Logeswari G; Thavavel Vaiyapuri; Tariq Ahamed Ahanger; Fadl Dahan; Fahima Hajjej; Ismail Keshta; Majed Alsafyani; Roobaea Alroobaea; Kaamran Raahemifar

doi:10.3389/fphys.2023.1143249

A convolutional neural network for face mask detection in IoT-based smart healthcare systems

Front Physiol. 2023 Mar 31:14:1143249. doi: 10.3389/fphys.2023.1143249. eCollection 2023.

Authors

Bose S¹, Logeswari G¹, Thavavel Vaiyapuri², Tariq Ahamed Ahanger³, Fadl Dahan^{4

5}, Fahima Hajjej⁵, Ismail Keshta⁶, Majed Alsafyani⁷, Roobaea Alroobaea⁷, Kaamran Raahemifar^{8

9

10}

Affiliations

¹ Department of Computer Science and Engineering, CEG Campus, Anna University, Chennai, Tamil Nadu, India.
² Department of Computer Sciences, College of Computer engineering and sciences, Prince Sattam Bin AbdulAziz University, Al-Kharj, Saudi Arabia.
³ Department of Management Information Systems, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
⁴ Department of Management Information Systems, College of Business Administration Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
⁵ Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
⁶ Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia.
⁷ Department of Computer Science, College of Computers and Information Technology, Taif University, Al-Hawiyya, Saudi Arabia.
⁸ College of Information Sciences and Technology, Data Science and Artificial Intelligence Program, State College, Penn State University, State College, PA, United States.
⁹ School of Optometry and Vision Science, Faculty of Science, University of Waterloo, Waterloo, ON, Canada.
¹⁰ Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.

Abstract

The new coronavirus that produced the pandemic known as COVID-19 has been going across the world for a while. Nearly every area of development has been impacted by COVID-19. There is an urgent need for improvement in the healthcare system. However, this contagious illness can be controlled by appropriately donning a facial mask. If people keep a strong social distance and wear face masks, COVID-19 can be controlled. A method for detecting these violations is proposed in this paper. These infractions include failing to wear a facemask and failing to maintain social distancing. To train a deep learning architecture, a dataset compiled from several sources is used. To compute the distance between two people in a particular area and also predicts the people wearing and not wearing the mask, The proposed system makes use of YOLOv3 architecture and computer vision. The goal of this research is to provide valuable tool for reducing the transmission of this contagious disease in various environments, including streets and supermarkets. The proposed system is evaluated using the COCO dataset. It is evident from the experimental analysis that the proposed system performs well in predicting the people wearing the mask because it has acquired an accuracy of 99.2% and an F1-score of 0.99.

Keywords: COVID-19; convolutio nal neural networks (CNN); face mask and social distancing detection; image processing; neural network.

Grants and funding

This study is supported from Prince sattam bin Abdulaziz University project number (PSAU/2023/R/1444) and supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R236), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.