Implementation of YOLO-v5 for a Real Time Social Distancing Detection

Abstract

The world is in an uproar with the Covid-19 pandemic, which has had an impact on society. Various efforts have been made by governments around the world to suppress the spread of the Covid-19 virus. One of the health protocols that have been appealed by the government is social distancing or social restrictions, namely limiting interactions between human beings as long as 1-2 meters. But in reality, there are still many people who ignore social distancing policies. The application of a social distancing detection system can be a solution to this problem. This system aims to detect people who are violating health protocols in the form of social distancing and then issue a voice warning to keep their distance from others to avoid the spread of the Covid-19 virus by using the YOLO-v5 method which is the latest version of YOLO (You Only Look Once). . Processing speed of YOLO-v5 has increased drastically with the fastest speed reaching 140 Frames Per Second (FPS) and has a small size, even having a size of 90% compared to the previous version. The accuracy of human detection using YOLO-v5 from this system reaches 83.28% and the accuracy of social distancing detection reaches 90.8%. From the results of the percentage analysis that has been carried out, it can be concluded that the system that has been created can function well for social distancing detection, but it is difficult to detect if humans are too far from the camera.

Keywords

• computer vision
• covid-19
• deep learning
• social distancing
• yolo