TY - GEN
T1 - Indonesian Traffic Sign Recognition for Advanced Driver Assistent (ADAS) Using YOLOv4
AU - Mulyanto, Agus
AU - Borman, Rohmat Indra
AU - Prasetyawan, Purwono
AU - Jatmiko, Wisnu
AU - Mursanto, Petrus
AU - Sinaga, Aprian
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/12/10
Y1 - 2020/12/10
N2 - Traffic violations are one of the causes of the increasing number of road traffic fatalities every year, apart from driver negligence or ignorance of traffic signs. ADAS does not totally forestall mishap, however they can all more likely shield us from a few human elements and human mistake. The goal of ADAS is to automate vehicle systems for better driving and safety, such as Traffic Sign Recognition (TSR). This paper presents a study to recognize traffic sign patterns using YOLOv4 using the Indonesia Traffic Signs (ITS) dataset. The ITS dataset consists of four categories (warning, prohibitory, mandatory and directive) with twenty six signs. The deep learning model of YOLOv4 is based CSP-DarkNet53 backbone which has shown good performance with main Average Precision ([email protected]) of 74.91% for 26 signs of Indonesian Traffic Signs.
AB - Traffic violations are one of the causes of the increasing number of road traffic fatalities every year, apart from driver negligence or ignorance of traffic signs. ADAS does not totally forestall mishap, however they can all more likely shield us from a few human elements and human mistake. The goal of ADAS is to automate vehicle systems for better driving and safety, such as Traffic Sign Recognition (TSR). This paper presents a study to recognize traffic sign patterns using YOLOv4 using the Indonesia Traffic Signs (ITS) dataset. The ITS dataset consists of four categories (warning, prohibitory, mandatory and directive) with twenty six signs. The deep learning model of YOLOv4 is based CSP-DarkNet53 backbone which has shown good performance with main Average Precision ([email protected]) of 74.91% for 26 signs of Indonesian Traffic Signs.
KW - Advanced Driver Assistance System (ADAS)
KW - Indonesian Traffic Sign Dataset
KW - Traffic Sign Detection
KW - Traffic Sign Recognition
KW - YOLOv4
UR - http://www.scopus.com/inward/record.url?scp=85100053513&partnerID=8YFLogxK
U2 - 10.1109/ISRITI51436.2020.9315368
DO - 10.1109/ISRITI51436.2020.9315368
M3 - Conference contribution
AN - SCOPUS:85100053513
T3 - 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020
SP - 520
EP - 524
BT - 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020
A2 - Wibowo, Ferry Wahyu
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020
Y2 - 10 December 2020
ER -