Journal of ICT Research and Applications ITB Bandung Vol. 14 No. 3 2020
A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS
Dublin Core
Title
Journal of ICT Research and Applications ITB Bandung Vol. 14 No. 3 2020
A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS
A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS
Subject
ADAS; convolutional neural network (CNN); Indonesian Traffic Obstacle Dataset; intelligent transport systems (ITS); YOLOv4.
Description
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.
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.
Creator
Agus Mulyanto, Wisnu Jatmiko, Petrus Mursanto, Purwono Prasetyawan & Rohmat Indra Borman
Source
DOI: 10.5614/itbj.ict.res.appl.2021.14.3.6
Publisher
IRCS-ITB
Date
23 Maret 2021
Contributor
Sri Wahyuni
Rights
ISSN: 2337-5787
Format
PDF
Language
English
Type
Text
Coverage
Journal of ICT Research and Applications ITB Bandung Vol. 14 No. 3 2020
Files
Collection
Citation
Agus Mulyanto, Wisnu Jatmiko, Petrus Mursanto, Purwono Prasetyawan & Rohmat Indra Borman, “Journal of ICT Research and Applications ITB Bandung Vol. 14 No. 3 2020
A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS,” Repository Horizon University Indonesia, accessed March 12, 2025, https://repository.horizon.ac.id/items/show/3387.
A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS,” Repository Horizon University Indonesia, accessed March 12, 2025, https://repository.horizon.ac.id/items/show/3387.