Advanced aerial monitoring and vehicle classification for intelligent transportation systems with YOLOv8 variants
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Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Academic Press Ltd- Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Aerial monitoring assumes a pivotal role within the domain of Intelligent Transportation Systems (ITS), imparting invaluable data and discernments that ameliorate the efficacy, security, and holistic operability of transportation networks. Image processing, encompassing the derivation of valuable insights through the manipulation of visual data captured by imaging apparatus, resides at the core and is poised to establish a firm footing in forthcoming ITS applications. In this context, numerous machine learning methodologies have been devised to enhance image processing, with novel approaches continually emerging. YOLOv8 emerged earlier this year and is still in the process of assimilating its potential application within the domain of ITS. In this study, a comprehensive assessment was conducted on all constituent variants of YOLOv8, specifically within the context of its application in the domain of aerial traffic monitoring. Using a custom-modified commercial drone, extensive datasets were acquired encompassing a diverse range of flight scenarios and traffic dynamics. To optimize model performance, meticulous consideration was given to ensuring dataset inclusivity, encompassing the full spectrum of vehicular typologies, while maintaining a homogeneous structure that accommodates an array of environmental nuances, including illumination and shading variations. The outcomes evince that both YOLOv8l and YOLOv8x exhibit notable superiority over other variants, manifesting exceptional detection efficacy even amid high-density traffic scenarios and the presence of obstructive elements. Contrastingly, in comparison to earlier iterations of YOLO, the current models demonstrate heightened precision in vehicle classification, yielding a reduction in misclassification instances. Although YOLOv8n exhibits a relatively subdued performance relative to other models, its potential is discernible in real-time applications, particularly within the purview of ITS, owing to its commendable proficiency in detection rates.
Açıklama
Anahtar Kelimeler
Aerial traffic monitoring, Vehicle detection, Intelligent transportation systems, YOLOv8, Smart traffic management, Convolutional neural networks, Deep learning
Kaynak
Journal of Network and Computer Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
237
