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All Journal Jurnal Buana Informatika Journal of ICT Research and Applications Jurnal Edukasi dan Penelitian Informatika (JEPIN) CESS (Journal of Computer Engineering, System and Science) Fountain of Informatics Journal Format : Jurnal Imiah Teknik Informatika JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah FIFO CIRCUIT: Jurnal Ilmiah Pendidikan Teknik Elektro INOVTEK Polbeng - Seri Informatika JMM (Jurnal Masyarakat Mandiri) SINTECH (Science and Information Technology) Journal Jurnal Teknoinfo ILKOM Jurnal Ilmiah J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) JURTEKSI IJISCS (International Journal Of Information System and Computer Science) Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CSRID (Computer Science Research and Its Development Journal) Jurnal Teknologi Komputer dan Sistem Informasi Jurnal Tekno Kompak Respati JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service J-SAKTI (Jurnal Sains Komputer dan Informatika) Insearch: Information System Research Journal JUSTIN (Jurnal Sistem dan Teknologi Informasi) International Journal of Informatics, Economics, Management and Science Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
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Journal : Journal of ICT Research and Applications

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.