YOLOv7 versus YOLOv8: A Comparative Study on Traffic Sign Detection Accuracy in Real-World Images

Basit Öğe Kaydı
dc.contributor.authorWojciechowski, Witold
dc.contributor.authorNowakowska, Leslawa M.
dc.contributor.authorZajac, Zofia G.
dc.contributor.authorKaczmarek, Walenty J.
dc.contributor.authorBoz, Ilayda
dc.date.accessioned2025-03-17T12:22:48Z
dc.date.available2025-03-17T12:22:48Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024 -- 21 September 2024 through 22 September 2024 -- Malatya -- 203423
dc.description.abstractAdvancements in technology have spurred the development of autonomous driving systems, necessitating new innovations to enhance road safety and address transportation challenges. This study evaluates and compares the performance of YOLOv7 and YOLOv8 models from the YOLO family in detecting traffic signs, which are crucial for road safety. Using a comprehensive dataset sourced from the Google Earth platform, the research examines the limitations and successes of both models. YOLOv8 demonstrates notable performance in traffic sign detection under challenging conditions, with precision, recall, and mAP values of 0.866,0.815, and 0.807, respectively. While YOLOv7 shows competitive results, it falls short of YOLOv8 in difficult scenarios. The study provides a detailed analysis of both algorithms, exploring their strengths, weaknesses, and areas for potential improvement in traffic sign detection. © 2024 IEEE.
dc.identifier.doi10.1109/IDAP64064.2024.10710941
dc.identifier.isbn979-833153149-2
dc.identifier.scopus2-s2.0-85207936850
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP64064.2024.10710941
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1400
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250316
dc.subjectdeep learning
dc.subjectobject detection
dc.subjectTraffic sign detection
dc.subjectYOLOv7
dc.subjectYOLOv8
dc.titleYOLOv7 versus YOLOv8: A Comparative Study on Traffic Sign Detection Accuracy in Real-World Images
dc.typeConference Object

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu