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

Research output: Contribution to journalArticle

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.
Original languageEnglish
Pages (from-to)286-298
Number of pages13
JournalJournal of ICT Research and Applications
Volume14
Issue number3
Publication statusPublished - 2021

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