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

Hamzah Abdulmalek Al-Haimi and Zamani Md Sani and Tarmizi Ahmad Izzudin and Hadhrami Abdul Ghani and Azizul Azizan and Samsul Ariffin Abdul Karim (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 (IJ-AI), 12 (4). pp. 1585-1592. ISSN 2089-4872

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URL: https://ijai.iaescore.com/index.php/IJAI/article/v...

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.

Item Type:	Article
Keyword:	Deep learning, Detection and recognition, Traffic counter, Traffic light, You only look once
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science T Technology > TE Highway engineering. Roads and pavements > TE1-450 Highway engineering. Roads and pavements > TE210-228.3 Construction details Including foundations, maintenance, equipment
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	SITI AZIZAH BINTI IDRIS -
Date Deposited:	05 Mar 2024 10:36
Last Modified:	05 Mar 2024 10:36
URI:	https://eprints.ums.edu.my/id/eprint/38443

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