TRAFİK İŞARETLERİNİN TESPİTİNDE FARKLI YOLO MODELLERİNİN KARŞILAŞTIRILMASI

Mehmet Fatih Bekçioğulları; Bünyamin Dikici; Hakan Açıkgöz; Serkan Özbay

doi:10.17780/ksujes.1524094

EN TR

THE COMPARISON OF DIFFERENT YOLO MODELS FOR TRAFFIC SIGN DETECTION

Abstract

Traffic signs are placed on highways to warn drivers travelling on the highway in terms of road restrictions. It is important for driving safety that these signs are correctly detected and that the restrictions required by the traffic sign are obeyed. In recent years, many studies have shown that deep learning algorithms are successful in object classification and detection. In this study, deep learning based ‘You Only Look Once’ (YOLO) algorithms are compared in terms of traffic sign detection. Firstly, a four-class traffic sign dataset consisting of 877 images is collected. Then, YOLOv5, YOLOv8 and YOLOv9 algorithms are analyzed in terms of traffic sign recognition. In the experimental studies, in order to evaluate the performance of the detection algorithms, recall, precision, f1 score and mAP performance evaluation criteria are analyzed. According to the experimental results obtained, the Recall metric of YOLOv9 is 90.8%, mAP@0.5 metric is 93.1% , and mAP@0.5:0.95 metric is 77.7%. These results validate that YOLOv9 is the best traffic sign detection algorithm.

Keywords

Kaynakça

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Ayrıntılar

Birincil Dil

Türkçe

Konular

Derin Öğrenme

Bölüm

Araştırma Makalesi

Yazarlar

Mehmet Fatih Bekçioğulları ^*
0000-0002-0056-9526
Türkiye

Bünyamin Dikici
0000-0001-6722-5894
Türkiye

Hakan Açıkgöz
0000-0002-6432-7243
Türkiye

Serkan Özbay
0000-0001-5973-8243
Türkiye

Yayımlanma Tarihi

3 Mart 2025

Gönderilme Tarihi

29 Temmuz 2024

Kabul Tarihi

2 Aralık 2024

Yayımlandığı Sayı

Yıl 1970 Cilt: 28 Sayı: 1

DOI

https://doi.org/10.17780/ksujes.1524094

IZ

https://izlik.org/JA59XA28FC

Kaynak Göster

RIS / Bibtex

APA

Bekçioğulları, M. F., Dikici, B., Açıkgöz, H., & Özbay, S. (2025). TRAFİK İŞARETLERİNİN TESPİTİNDE FARKLI YOLO MODELLERİNİN KARŞILAŞTIRILMASI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 138-150. https://doi.org/10.17780/ksujes.1524094

Cited By

Helmet Detection System with YOLO Architecture in Real-Time Construction Site Cameras

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi

https://doi.org/10.54365/adyumbd.1757010

THE COMPARISON OF DIFFERENT YOLO MODELS FOR TRAFFIC SIGN DETECTION

Abstract

Keywords