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Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5

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...

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Main Authors: Al-Haimi, Hamzah Abdulmalek, Md. Sani, Zamani, Ahmad Izzudin, Tarmizi, Abdul Ghani, Hadhrami, Azizan, Azizul, Abdul Karim, Samsul Ariffin
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2023
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/107585/1/AzizulAzizan2023_TrafficLightCounterDetectionComparison.pdf
http://eprints.utm.my/107585/
http://dx.doi.org/10.11591/ijai.v12.i4.pp1585-1592
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spelling my.utm.1075852024-09-25T06:21:31Z http://eprints.utm.my/107585/ Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5 Al-Haimi, Hamzah Abdulmalek Md. Sani, Zamani Ahmad Izzudin, Tarmizi Abdul Ghani, Hadhrami Azizan, Azizul Abdul Karim, Samsul Ariffin T Technology (General) 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. Institute of Advanced Engineering and Science 2023 Article PeerReviewed application/pdf en http://eprints.utm.my/107585/1/AzizulAzizan2023_TrafficLightCounterDetectionComparison.pdf Al-Haimi, Hamzah Abdulmalek and Md. Sani, Zamani and Ahmad Izzudin, Tarmizi and Abdul Ghani, Hadhrami and Azizan, Azizul and Abdul Karim, Samsul Ariffin (2023) Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5. IAES International Journal of Artificial Intelligence, 12 (4). pp. 1585-1592. ISSN 2089-4872 http://dx.doi.org/10.11591/ijai.v12.i4.pp1585-1592 DOI : 10.11591/ijai.v12.i4.pp1585-1592
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Al-Haimi, Hamzah Abdulmalek
Md. Sani, Zamani
Ahmad Izzudin, Tarmizi
Abdul Ghani, Hadhrami
Azizan, Azizul
Abdul Karim, Samsul Ariffin
Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5
description 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.
format Article
author Al-Haimi, Hamzah Abdulmalek
Md. Sani, Zamani
Ahmad Izzudin, Tarmizi
Abdul Ghani, Hadhrami
Azizan, Azizul
Abdul Karim, Samsul Ariffin
author_facet Al-Haimi, Hamzah Abdulmalek
Md. Sani, Zamani
Ahmad Izzudin, Tarmizi
Abdul Ghani, Hadhrami
Azizan, Azizul
Abdul Karim, Samsul Ariffin
author_sort Al-Haimi, Hamzah Abdulmalek
title Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5
title_short Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5
title_full Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5
title_fullStr Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5
title_full_unstemmed Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5
title_sort traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5
publisher Institute of Advanced Engineering and Science
publishDate 2023
url http://eprints.utm.my/107585/1/AzizulAzizan2023_TrafficLightCounterDetectionComparison.pdf
http://eprints.utm.my/107585/
http://dx.doi.org/10.11591/ijai.v12.i4.pp1585-1592
_version_ 1811681228434178048
score 13.211869

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