Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/4629
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dc.contributor.authorBudak, S.-
dc.contributor.authorBozkaya, F.-
dc.contributor.authorAkmaz, M.Y.-
dc.contributor.authorTiglioglu, S.-
dc.contributor.authorBoynukara, C.-
dc.contributor.authorKazancı, O.-
dc.contributor.authorBudak, Z.H.Y.-
dc.date.accessioned2023-10-02T11:17:35Z-
dc.date.available2023-10-02T11:17:35Z-
dc.date.issued2023-
dc.identifier.isbn9798350321388-
dc.identifier.urihttps://doi.org/10.1109/ECAI58194.2023.10193941-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/4629-
dc.descriptionOrange TM;STCen_US
dc.description15th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2023 -- 29 June 2023 through 30 June 2023 -- 191300en_US
dc.description.abstractAutonomous vehicles use many technologies and methods to detect and act on surrounding objects. The most common among these technologies is an algorithm called YOLO (You Only Look Once). This algorithm quickly detects objects in an image and classifies these objects accurately. This study examines the use of the YOLO algorithm for signage detection in autonomous vehicles and how this algorithm can be improved. First of all, the basic principles and working mechanisms of the YOLO algorithm are explained. Then, it is explained in detail how this algorithm can be used for plate detection in autonomous vehicles. Various models were trained using the YOLO algorithm and the data set created with real data, and the trained models were tested on real-time systems. Finally, suggestions for the improvement of the YOLO algorithm are presented and how this algorithm can be improved further in the future is discussed. © 2023 IEEE.en_US
dc.description.sponsorshipB022300751; Konya Teknik Üniversitesi, KTÜN; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAKen_US
dc.description.sponsorshipThis article is supported by TUBITAK 2224-A Program (App.No:1919B022300751). The authors would like to thank TUBITAK and Konya Technical University RAC-LAB Research Laboratory (http://www.rac-lab.com).en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof15th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2023 - Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAutonomous vehicles; Data replication; Enhancement automatic feature recognition; Image processing; Plate detection; YOLO algorithmen_US
dc.subjectImage enhancement; Interactive computer systems; Object detection; Real time systems; Traffic signs; Automatic feature recognition; Autonomous Vehicles; Data replication; Enhancement automatic feature recognition; Images processing; Plate detections; Replication method; Traffic sign detection; Vehicle use; You only look once algorithm; Autonomous vehiclesen_US
dc.titleUse of Yolo Algorithm for Traffic Sign Detection in Autonomous Vehicles and Improvement Using Data Replication Methodsen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/ECAI58194.2023.10193941-
dc.identifier.scopus2-s2.0-85168110061en_US
dc.departmentKTÜNen_US
dc.institutionauthor-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.authorscopusid57220895614-
dc.authorscopusid58538957200-
dc.authorscopusid58538314200-
dc.authorscopusid58538473600-
dc.authorscopusid58538473700-
dc.authorscopusid58539119000-
dc.authorscopusid58538638800-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.openairetypeConference Object-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
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