Advanced aerial monitoring and vehicle classification for intelligent transportation systems with YOLOv8 variants

Basit Öğe Kaydı
dc.contributor.authorBakirci, Murat
dc.date.accessioned2025-03-17T12:27:03Z
dc.date.available2025-03-17T12:27:03Z
dc.date.issued2025
dc.departmentTarsus Üniversitesi
dc.description.abstractAerial 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.
dc.identifier.doi10.1016/j.jnca.2025.104134
dc.identifier.issn1084-8045
dc.identifier.issn1095-8592
dc.identifier.scopus2-s2.0-85217392148
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.jnca.2025.104134
dc.identifier.urihttps://hdl.handle.net/20.500.13099/2047
dc.identifier.volume237
dc.identifier.wosWOS:001427753200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBakirci, Murat
dc.language.isoen
dc.publisherAcademic Press Ltd- Elsevier Science Ltd
dc.relation.ispartofJournal of Network and Computer Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectAerial traffic monitoring
dc.subjectVehicle detection
dc.subjectIntelligent transportation systems
dc.subjectYOLOv8
dc.subjectSmart traffic management
dc.subjectConvolutional neural networks
dc.subjectDeep learning
dc.titleAdvanced aerial monitoring and vehicle classification for intelligent transportation systems with YOLOv8 variants
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu