Open AccessPemantauan Physical Distance Pada Area Umum Menggunakan YOLO Tiny V3
Author(s) -
Mohammad Chasrun Hasani,
Fadhila Milenasari,
Novendra Setyawan
Publication year - 2022
Publication title -
jurnal resti (rekayasa sistem dan teknologi informasi)
Language(s) - English
Resource type - Journals
ISSN - 2580-0760
DOI - 10.29207/resti.v6i1.3808
Subject(s) - preprocessor , euclidean distance , computer science , artificial intelligence , covid-19 , computer vision , geography , pattern recognition (psychology) , infectious disease (medical specialty) , medicine , disease , pathology
Coronavirus disease in 2019 (Covid-19) is a phenomenon that become to the world concern because almost all countries experience the outbreak. One of attention to preventing the spread of Covid-19 is the physical distance in public areas. This study proposes human detection in public spaces by using image processing. The application of physical distance is intended to monitor the distance between people in public places. In this study, a human detection system is done by using the YOLO Tiny V3 method and the Euclidean algorithm to be developed to detect distances between humans. There are several stages in the research process: data collection, data preprocessing, data training, and physical distance detection. The system that has been designed can detect by getting an accuracy result of 78.43% for detecting human objects and an accuracy result of 87.82% for detecting distances between humans.