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A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS

Journal of ICT Research and Applications (JICTRA) · Vol. 14 No. 3 (2021) · pp. 286–298

DOI
10.5614/itbj.ict.res.appl.2021.14.3.6
ISSN
2338-5499, 2337-5787
Published
2021-03-31
Publisher
DRI - ITB
Language
eng

Abstract

Intelligent transport systems (ITS) are a promising area of studies. One implementation of ITS are advanced driver assistance systems (ADAS), involving the problem of obstacle detection in traffic. This study evaluated the YOLOv4 model as a state-of-the-art CNN-based one-stage detector to recognize traffic obstacles. A new dataset is proposed containing traffic obstacles on Indonesian roads for ADAS to detect traffic obstacles that are unique to Indonesia, such as pedicabs, street vendors, and bus shelters, and are not included in existing datasets. This study established a traffic obstacle dataset containing eleven object classes: cars, buses, trucks, bicycles, motorcycles, pedestrians, pedicabs, trees, bus shelters, traffic signs, and street vendors, with 26,016 labeled instances in 7,789 images. A performance analysis of traffic obstacle detection on Indonesian roads using the dataset created in this study was conducted using the YOLOv4 method.

Keywords

Collaboration network of the authors

31 authors · 57 co-authorship links · 14 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Indonesia
  • Universitas Teknokrat Indonesia
  • Politeknik Negeri Batam
  • Tarumanagara University
  • Jenderal Soedirman University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Mgs M Luthfi RamadhanUniversity of Indonesia3
Adila Alfa KrisnadhiUniversity of Indonesia2
N. W. PambudiUniversity of Indonesia2
R. M. MardianAffiliation unknown1
RochmatullahAffiliation unknown1
H. R. SanabilaUniversity of Indonesia1
Muntasir RahmanUniversity of Indonesia1
M. Sakti AlvissalimAffiliation unknown1
A. FebrianUniversity of Indonesia1
R. Y. S. DhiemasAffiliation unknown1
Sani Muhammad IsaTarumanagara University1
Robeth RahmatullahUniversity of Indonesia1
Ikhsanul HabibieUniversity of Indonesia1
Grafika JatiUniversity of Indonesia1
Siti Hadiyan PratiwiUniversity of Indonesia1
Putri ShaniyaUniversity of Indonesia1
Fityan AziziUniversity of Indonesia1
Lhuqita FazryUniversity of Indonesia1
Gregory TridityaUniversity of Indonesia1
Dedy Van HautenUniversity of Indonesia1
Muhammad Hannan HunafaUniversity of Indonesia1
Anas Bayu KusumaUniversity of Indonesia1
Yogiek Indra KurniawanJenderal Soedirman University1
Vektor DewantoUniversity of Indonesia1
Dwi Ely KurniawanPoliteknik Negeri Batam1
Nanda Rachmat HartadiPoliteknik Negeri Batam1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS</dc:title>
	<dc:creator>Mulyanto, Agus</dc:creator>
	<dc:creator>Jatmiko, Wisnu</dc:creator>
	<dc:creator>Mursanto, Petrus</dc:creator>
	<dc:creator>Prasetyawan, Purwono</dc:creator>
	<dc:creator>Borman, Rohmat Indra</dc:creator>
	<dc:subject xml:lang="en-US">ADAS</dc:subject>
	<dc:subject xml:lang="en-US">convolutional  neural  network(CNN)</dc:subject>
	<dc:subject xml:lang="en-US">Indonesian  Traffic  Obstacle Dataset</dc:subject>
	<dc:subject xml:lang="en-US">Intelligent transport systems (ITS)</dc:subject>
	<dc:subject xml:lang="en-US">YOLOv4</dc:subject>
	<dc:description xml:lang="en-US">Intelligent transport systems (ITS) are a promising area of studies. One implementation of ITS are advanced driver assistance systems (ADAS), involving the problem of obstacle detection in traffic. This study evaluated the YOLOv4 model as a state-of-the-art CNN-based one-stage detector to recognize traffic obstacles. A new dataset is proposed containing traffic obstacles on Indonesian roads for ADAS to detect traffic obstacles that are unique to Indonesia, such as pedicabs, street vendors, and bus shelters, and are not included in existing datasets. This study established a traffic obstacle dataset containing eleven object classes: cars, buses, trucks, bicycles, motorcycles, pedestrians, pedicabs, trees, bus shelters, traffic signs, and street vendors, with 26,016 labeled instances in 7,789 images. A performance analysis of traffic obstacle detection on Indonesian roads using the dataset created in this study was conducted using the YOLOv4 method.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2021-03-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/12752</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2021.14.3.6</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 14 No. 3 (2021); 286-298</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/12752/5149</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Journal of ICT Research and Applications</dc:rights>
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