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

Authors

  • Agus Mulyanto Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
  • Wisnu Jatmiko Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia
  • Petrus Mursanto Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia
  • Purwono Prasetyawan Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
  • Rohmat Indra Borman Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia

DOI:

https://doi.org/10.5614/itbj.ict.res.appl.2021.14.3.6

Keywords:

ADAS, convolutional neural network(CNN), Indonesian Traffic Obstacle Dataset, Intelligent transport systems (ITS), YOLOv4

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.

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Published

2021-03-31

How to Cite

Mulyanto, A., Jatmiko, W., Mursanto, P., Prasetyawan, P., & Borman, R. I. (2021). A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS. Journal of ICT Research and Applications, 14(3), 286-298. https://doi.org/10.5614/itbj.ict.res.appl.2021.14.3.6

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