Title: Real-time face mask position recognition system using YOLO models for preventing COVID-19 disease spread in public places

Authors: Vishnu Kumar Kaliappan; Rajasekaran Thangaraj; P. Pandiyan; K. Mohanasundaram; S. Anandamurugan; Dugki Min

Addresses: Konkuk Aerospace Design-Airworthiness Research Institute, Konkuk University, Seoul, 05029, South Korea ' Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, 641047, Tamil Nadu, India ' Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, 641047, Tamil Nadu, India ' Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, 641047, Tamil Nadu, India ' Department of Information Technology, Kongu Engineering College, 638052, Tamil Nadu, India ' Department of Computer Science and Engineering, Konkuk University, Seoul, 05029, South Korea

Abstract: The COVID-19 pandemic has infected tens of millions of individuals around the world, and it is currently posing a worldwide health calamity. Wearing a face mask in public places is one of the most effective protection strategies, according to the World Health Organization (WHO). Moreover, their effectiveness has declined due to incorrect use of the face mask. In this scenario, effective recognition systems are anticipated to ensure that people's faces are covered with masks in public locations. Many people do not correctly wear the masks due to inadequate practices, undesirable behaviour, or individual vulnerabilities. As a result, there has been an increase in demand for automatic real-time face mask detection and mask position detection to substitute manual reminders. This proposed work classifies people into three categories such as with mask, without mask and mask with incorrect position. The dataset is tested using three different variants of object detection models, namely YOLOv4, Tiny YOLOv4, and YOLOv5. The experimental result shows that YOLOv5 model outperforms with the highest mAP value of 99.40% compared to the other two models.

Keywords: YOLO; mask position; object detection model; COVID-19; social distancing.

DOI: 10.1504/IJAHUC.2023.128499

International Journal of Ad Hoc and Ubiquitous Computing, 2023 Vol.42 No.2, pp.73 - 82

Received: 12 Jan 2022
Received in revised form: 17 Jun 2022
Accepted: 03 Aug 2022
Published online: 24 Jan 2023 *

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