Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/4936
Title: Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5
Authors: Al-Haimi, H. A. 
Sani, Z. M. 
Izzudin, T. A. 
Ab Ghani, H. 
Azizan, A. 
Karim, S. A. A. 
Keywords: Deep learning;Detection and recognition;Traffic counter;Traffic light;You only look once
Issue Date: 2023
Publisher: Institute of Advanced Engineering and Science
Journal: IAES International Journal of Artificial Intelligence (IJ-AI) 
Abstract: 
This project aims to develop a vision system that can detect traffic light counter and to recognise the numbers shown on it. The system used you only look once version 3 (YOLOv3) algorithm because of its robust performance and reliability and able to be implemented in Nvidia Jetson nano kit. A total of 2204 images consisting of numbers from 0-9 green and 0-9 red. Another 80% (1764) from the images are used for training and 20% (440) are used for testing. The results obtained from the training demonstrated Total precision=89%, Recall=99.2%, F1 score=70%, intersection over union (IoU)=70.49%, mean average precision (mAp)=87.89%, Accuracy=99.2% and the estimate total confidence rate for red and green are 98.4% and 99.3% respectively. The results were compared with the previous YOLOv5 algorithm, and the results are substantially close to each other as the YOLOv5 accuracy and recall at 97.5% and 97.5% respectively.
Description: 
Scopus
URI: http://hdl.handle.net/123456789/4936
ISSN: 20894872
DOI: 10.11591/ijai.v12.i4.pp1585-1592
Appears in Collections:Faculty of Data Science and Computing - Journal (Scopus/WOS)

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