Hamzah Abdulmalek Al-Haimi, Z. Sani, Tarmizi Ahmad Izzudin, Hadhrami Abdul Ghani, A. Azizan, Samsul Ariffin Abdul Karim
{"title":"Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5","authors":"Hamzah Abdulmalek Al-Haimi, Z. Sani, Tarmizi Ahmad Izzudin, Hadhrami Abdul Ghani, A. Azizan, Samsul Ariffin Abdul Karim","doi":"10.11591/ijai.v12.i4.pp1585-1592","DOIUrl":null,"url":null,"abstract":"This project aims to develop a vision system that can detect traffic lightcounter and to recognise the numbers shown on it. The system used you onlylook once version 3 (YOLOv3) algorithm because of its robust performanceand reliability and able to be implemented in Nvidia Jetson nano kit. A totalof 2204 images consisting of numbers from 0-9 green and 0-9 red. Another80% (1764) from the images are used for training and 20% (440) are used fortesting. The results obtained from the training demonstrated Totalprecision=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 YOLOv5algorithm, and the results are substantially close to each other as the YOLOv5accuracy and recall at 97.5% and 97.5% respectively.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IAES International Journal of Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11591/ijai.v12.i4.pp1585-1592","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Decision Sciences","Score":null,"Total":0}
引用次数: 0
Abstract
This project aims to develop a vision system that can detect traffic lightcounter and to recognise the numbers shown on it. The system used you onlylook once version 3 (YOLOv3) algorithm because of its robust performanceand reliability and able to be implemented in Nvidia Jetson nano kit. A totalof 2204 images consisting of numbers from 0-9 green and 0-9 red. Another80% (1764) from the images are used for training and 20% (440) are used fortesting. The results obtained from the training demonstrated Totalprecision=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 YOLOv5algorithm, and the results are substantially close to each other as the YOLOv5accuracy and recall at 97.5% and 97.5% respectively.
该项目旨在开发一种视觉系统,可以检测交通灯计数器并识别其上显示的数字。该系统使用了你只看一次版本3 (YOLOv3)算法,因为它具有强大的性能和可靠性,并且能够在Nvidia Jetson纳米套件中实现。总共2204张图像,由0-9绿色和0-9红色的数字组成。另外80%(1764)的图像用于训练,20%(440)用于测试。训练结果表明:Totalprecision=89%, Recall=99.2%, F1得分=70%,intersection over union(IoU)=70.49%, mean average precision (mAp)=87.89%,准确率=99.2%,对红色和绿色的估计总置信度分别为98.4%和99.3%。将结果与之前的yolov5算法进行比较,结果基本接近,yolov5的准确率和召回率分别为97.5%和97.5%。