Autonomous Robots for Deep Mask-Wearing Detection in Educational Settings during Pandemics

Abstract

The COVID-19 pandemic has severely impacted various aspects of life, where countries closed their borders, and workplaces and educational institutions shut down their premises in response to lockdowns. This has adversely affected the lives of everyone, including millions of students worldwide, socially, mentally, and physically. Governments and educational authorities worldwide have taken preventive measures, such as social distancing and mask wearing, to control the spread of the virus. This paper proposes an AI-powered autonomous robot for deep mask-wearing detection to enforce proper mask wearing in educational settings. The system includes (1) Simultaneous Localization and Mapping framework to map and navigate the environment (i.e., laboratories and classrooms), (2) a multiclass face mask detection software, and (3) an auditory system to identify and alert improper or no mask wearing. We train our face mask detector using MobileNetV2 architecture and YOLOv2 object detector classification. The results demonstrate that our robot can navigate an educational environment while avoiding obstacles to detect violations. The proposed face mask detection and classification subsystem achieved a 91.4% average precision when tested on students in an engineering laboratory environment.