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A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS
- Source :
- Journal of ICT Research and Applications, Vol 14, Iss 3 (2021)
- Publication Year :
- 2021
- Publisher :
- ITB Journal Publisher, 2021.
-
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.
- Subjects :
- Truck
Information Systems and Management
General Computer Science
Computer science
Real-time computing
020207 software engineering
Advanced driver assistance systems
02 engineering and technology
TK5101-6720
Information technology
Object (computer science)
T58.5-58.64
language.human_language
Indonesian
Obstacle
0202 electrical engineering, electronic engineering, information engineering
language
Telecommunication
020201 artificial intelligence & image processing
Electrical and Electronic Engineering
Intelligent transportation system
Subjects
Details
- Language :
- English
- ISSN :
- 23385499 and 23375787
- Volume :
- 14
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- Journal of ICT Research and Applications
- Accession number :
- edsair.doi.dedup.....7fbdf1e538dea67db91fcbe9e4c50b3f