Developing Smart COVID-19 Social Distancing Surveillance Drone using YOLO Implemented in Robot Operating System simulation environment

Abstract

The Novel Coronavirus, termed as COVID-19 outbreak, is faced by almost all countries in the world. It spread through communal interaction between people, especially in densely populated areas. An effort to prevent Covid-19 transmission is social distancing regulation. However, this policy is not obeyed by the public, so the government needs to supervise the movement and people's interaction. The government needs a crowd surveillance system that can detect people's presence, identify the crowd, and give social distancing warnings. Therefore, we propose a drone that has the ability of localization, navigation, people detection, crowd identifier, and social distancing warning. We utilize YOLO-v3 to detect people and define adaptive social distancing detector. In this paper, we implemented a road segmentation on the IRIS PX4 drone in the Robot Operating System and Gazebo simulation. The proposed system also successfully demonstrated people and crowd detection with varying degrees of the crowd. The system obtained crowd detection accuracy is around 90% and expected to be readily implemented on real hardware drones and tested in real environments.