Open AccessYOLO Algorithm Accuracy Analysis in Detecting Amount of Vehicles at the Intersection
Author(s) -
N Dewantoro,
Pbc Fernando,
Tan Shi
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/426/1/012164
Subject(s) - intersection (aeronautics) , computer science , algorithm , artificial intelligence , transport engineering , engineering
The goal of this research is to find out YOLO algorithm’s effectiveness on detecting the number of vehicle on road. Our activity in this research is conduct training using a dataset that we created ourselves and do traffic recording simulation in a lot of scenario using YOLO original datasets and our own datasets. The result of this research is YOLO algorithm successfully detects vehicles as much as 65.3% of the total vehicles passing on the highway and gives the wrong label as much as 20.7% of the total label given if using YOLO original dataset. YOLO algorithm successfully detects vehicles as much as 9,3% of the total vehicles passing on the highway and gives the wrong label as much as 7,4% of the total label given if using our own dataset.