Open Access

ARTICLE

Deep Learning Based Face Mask Detection in Religious Mass Gathering During COVID-19 Pandemic

Abdullah S. AL-Malaise AL-Ghamdi^1,2,3, Sultanah M. Alshammari^3,4, Mahmoud Ragab^3,5,6,*

1 Information Systems Department, Faculty of Computing and Information Technology King Abdulaziz University, Jeddah, 21589, Saudi Arabia
2 Information Systems Department, HECI School, Dar Alhekma University, Jeddah, Saudi Arabia
3 Center of Excellence in Smart Environment Research, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
4 Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
5 Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
6 Department of Mathematics, Faculty of Science, Al-Azhar University, Naser City, Cairo, 11884, Egypt

* Corresponding Author: Mahmoud Ragab. Email:

Computer Systems Science and Engineering 2023, 46(2), 1863-1877. https://doi.org/10.32604/csse.2023.035869

Received 07 September 2022; Accepted 08 December 2022; Issue published 09 February 2023

Abstract

Notwithstanding the religious intention of billions of devotees, the religious mass gathering increased major public health concerns since it likely became a huge super spreading event for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Most attendees ignored preventive measures, namely maintaining physical distance, practising hand hygiene, and wearing facemasks. Wearing a face mask in public areas protects people from spreading COVID-19. Artificial intelligence (AI) based on deep learning (DL) and machine learning (ML) could assist in fighting covid-19 in several ways. This study introduces a new deep learning-based Face Mask Detection in Religious Mass Gathering (DLFMD-RMG) technique during the COVID-19 pandemic. The DLFMD-RMG technique focuses mainly on detecting face masks in a religious mass gathering. To accomplish this, the presented DLFMD-RMG technique undergoes two pre-processing levels: Bilateral Filtering (BF) and Contrast Enhancement. For face detection, the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 detector. In addition, the face detection performance can be improved by the seeker optimization algorithm (SOA) for tuning the hyperparameter of the ResNet-50 module, showing the novelty of the work. At last, the faces with and without masks are classified using the Fuzzy Neural Network (FNN) model. The stimulation study of the DLFMD-RMG algorithm is examined on a benchmark dataset. The results highlighted the remarkable performance of the DLFMD-RMG model algorithm in other recent approaches.

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Keywords

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