Indonesian Traffic Sign Recognition for Advanced Driver Assistent (ADAS) Using YOLOv4

Agus Mulyanto, Rohmat Indra Borman, Purwono Prasetyawan, Wisnu Jatmiko, Petrus Mursanto, Aprian Sinaga

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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 (mAP@0.5) of 74.91% for 26 signs of Indonesian Traffic Signs.

Original languageEnglish
Title of host publication2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020
EditorsFerry Wahyu Wibowo
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages520-524
Number of pages5
ISBN (Electronic)9781728184067
DOIs
Publication statusPublished - 10 Dec 2020
Event3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020 - Yogyakarta, Indonesia
Duration: 10 Dec 2020 → …

Publication series

Name2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020

Conference

Conference3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020
CountryIndonesia
CityYogyakarta
Period10/12/20 → …

Keywords

  • Advanced Driver Assistance System (ADAS)
  • Indonesian Traffic Sign Dataset
  • Traffic Sign Detection
  • Traffic Sign Recognition
  • YOLOv4

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