Real-Time Vehicle Detection Using YOLOv8-Nano for Intelligent Transportation Systems
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Tarih
2024
Yazarlar
Bakirci, Murat
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Int Information & Engineering Technology Assoc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In recent years, deep learning models have seen extensive use in various domains, with the YOLO algorithm family emerging as a prominent player. YOLOv5, known for its real-time object detection capabilities and high accuracy, has been widely embraced in transportation- related research. However, the introduction of YOLOv8 in early 2023 signifies a significant leap forward in object detection technology. Despite its potential, the literature on YOLOv8 remains relatively scarce, leaving room for exploration and adoption in research. This study pioneers real-time vehicle detection using the YOLOv8 algorithm. An in-depth analysis of YOLOv8n, the smallest scale model within the YOLOv8 series, was conducted to assess its suitability for real-time scenarios, particularly in Intelligent Transportation Systems (ITS). To reinforce its real-time capabilities, a parametric analysis covering image processing time, detection sensitivity, and input image characteristics was performed. To optimize model performance, a training dataset was created through flight tests using a custom autonomous drone, encompassing various vehicle variations. This ensures that the model excels in recognizing diverse motor vehicle configurations. The results reveal that even this compact sub-model achieves an impressive detection accuracy rate exceeding 80%. The study establishes that YOLOv8n, evaluated for the first time in ITS applications, effectively serves as an object detector for real-time smart traffic management.
Açıklama
Anahtar Kelimeler
vehicle detection, YOLOv8, aerial monitoring, intelligent transportation systems, UAV
Kaynak
Traitement Du Signal
WoS Q Değeri
Q4
Scopus Q Değeri
N/A
Cilt
41
Sayı
4
