Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/4936
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dc.contributor.authorAl-Haimi, H. A.en_US
dc.contributor.authorSani, Z. M.en_US
dc.contributor.authorIzzudin, T. A.en_US
dc.contributor.authorAb Ghani, H.en_US
dc.contributor.authorAzizan, A.en_US
dc.contributor.authorKarim, S. A. A.en_US
dc.date.accessioned2023-10-16T02:38:15Z-
dc.date.available2023-10-16T02:38:15Z-
dc.date.issued2023-
dc.identifier.issn20894872-
dc.identifier.urihttp://hdl.handle.net/123456789/4936-
dc.descriptionScopusen_US
dc.description.abstractThis 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.en_US
dc.language.isoenen_US
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofIAES International Journal of Artificial Intelligence (IJ-AI)en_US
dc.subjectDeep learningen_US
dc.subjectDetection and recognitionen_US
dc.subjectTraffic counteren_US
dc.subjectTraffic lighten_US
dc.subjectYou only look onceen_US
dc.titleTraffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5en_US
dc.typeInternationalen_US
dc.identifier.doi10.11591/ijai.v12.i4.pp1585-1592-
dc.description.fundingUniversiti Teknikal Malaysia Melaka (UTeM) through Facilitation Research Program by Research & Innovation Management (CRIM).en_US
dc.description.page1585-1592en_US
dc.description.researchareaComputer visionen_US
dc.volume12(4)en_US
dc.description.typeArticleen_US
item.fulltextWith Fulltext-
item.openairetypeInternational-
item.languageiso639-1en-
item.grantfulltextopen-
crisitem.author.deptUNIVERSITI MALAYSIA KELANTAN-
Appears in Collections:Faculty of Data Science and Computing - Journal (Scopus/WOS)
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scopusresults-Traffic light counter detection.pdf63.54 kBAdobe PDFView/Open
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