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All Journal Journal of ICT Research and Applications
Petrus Mursanto
Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia
Computer Science & IT

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A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS Agus Mulyanto; Wisnu Jatmiko; Petrus Mursanto; Purwono Prasetyawan; Rohmat Indra Borman
Journal of ICT Research and Applications Vol. 14 No. 3 (2021)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2021.14.3.6

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.
Co-Authors Agus Mulyanto Prasetyawan, Purwono Rohmat Indra Borman Wisnu Jatmiko