基于YOLO - v5的实时社交距离检测的实现（英文版）

Journal of Applied Informatics and Computing (JAIC)

Vol.6, No.1, Juli 2022, pp. 01~06

e-ISSN: 2548-6861 1

http://jurnal.polibatam.ac.id/index.php/JAIC

Implementation of YOLO-v5 for a real-time Social Distancing Detection

Falah Hikamudin Arby

*, Imam Husni Al Amin

Article Info

ABSTRACT

Received 2021-10-13

Revised 2021-12-17

Accepted 2022-02-14

The world is in an uproar with the Covid-19 pandemic, which has impacted

society. Governments have made various efforts worldwide to suppress the spread

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). The processing speed of YOLO-v5 has increased drastically, with the

fastest speed reaching 140 Frames Per Second (FPS). It 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 carried out, it can be concluded that the system created can function well

for social distancing detection. However, it is not easy to detect if humans are too

far from the camera.

I. INTRODUCTION

The Coronavirus disease 2019 (Covid-19) outbreak is a

that this virus infected 215,714,824 people, with 4,490,753

deaths worldwide [2].

The Covid-19 virus has a high level of infection to be

transmitted to people around it. It is known that the main

route of the spread of this virus is through liquid droplets

that attack the respiratory tract [3]. Therefore, both in

Indonesia and worldwide, the government urges the public

to implement health protocols, including wearing masks

when travelling, constantly washing hands, and

implementing physical distancing and social distancing.