Detection of Covid-19 Using an Infrared Fever Screening System (IFSS) Based on Deep Learning Technology

Abstract

Treatment for the new Coronavirus is both expensive and challenging, preventing infections may have a significant impact on healthcare costs and quality of life. This research presents a novel paradigm for identifying people displaying symptoms of COVID-19 infection in public settings, one that makes use of Convolution Neural Network (CNN) and deep machine learning methods. Infection with the COVID-19 virus is characterized by a high temperature, or fever. Accordingly, the goal of this research is to develop a prototype for an automated system that can detect and isolate COVID-19 carriers in public settings. The study’s suggested prototype is novel and cutting-edge in many ways: Our system consists of three components: (1) An Infrared Fever Screening System that automatically detects thermal signals and measures temperature to check whether an individual has a fever or not; (2) HD visual/facial auto-calibration system that provides accurate and presides facial landmarks that help track people and measure their temperature at various regions; (3) A real-time sensor fusion of visual and thermal camera data that forms a single model with multiple lidars, racial recognition. The proposed prototype accurately detects people with temperatures above average, even when an individual has a face mask. It balances the strengths of different sensors that can detect human body temperature from facial landmarks such as the forehead and the eyes.