Artificial Intelligence based Integrated and Distributed System for Preventing Covid-19 Spread Using Deep Learning

Abstract

The COVID-19 pandemic is causing a worldwide emergency in healthcare. This virus mainly spreads through droplets which emerge from a person infected with coronavirus and poses a risk to others. The risk of transmission is highest in public places. Many measures have been suggested, such as maintaining a social distance, and wearing a face mask to avoid the spread of this virus. There are three modules in this work, in the first module a mask detection system which detects whether a person wears a mask or not using deep learning techniques such as MobileNet V2 architecture along with Facenet and Masknet. Accuracy of 98.6% is achieved in this module with one or two people in the frame. Barricade has been set which does not allow people who does not wear mask and allows people who wears a mask. LED light indicators and LCD displays are used as alerts, and they are programmed to provide information that is both worn and not worn, depending on the output. In the second module, a system has been designed which detects the temperature of the person and detects whose temperature is above normal body temperature and alerts accordingly. In the third module a social distancing system has been designed which detects people who does not follow social distancing protocol and alerts them using deep learning techniques. The YOLOv3 algorithm is used which creates a square box around people that displays green or red color box according to the measurement output. The transfer learning methodology is also implemented to increase the accuracy of this module. The accuracy of 98.2% is achieved for social distance detection module using YOLOv3 detection model with transfer learning. All three modules are integrated so it automatically monitors human body temperature, detects mask and social distancing at the barricade system.