Refining Transportation Automation with Convolutional Neural Network-Based Vehicle Detection via UAVs

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dc.contributor.authorBakirci, Murat
dc.contributor.authorBayraktar, Irem
dc.date.accessioned2025-03-17T12:22:52Z
dc.date.available2025-03-17T12:22:52Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description2024 International Russian Automation Conference, RusAutoCon 2024 -- 8 September 2024 through 14 September 2024 -- Sochi -- 203125
dc.description.abstractIn response to the surge in private vehicle usage exacerbated by the pandemic, transportation infrastructure faces heightened demands for efficiency and safety. This trend, fueled by concerns over social distancing and changes in work patterns, has intensified traffic congestion in urban centers and tourist destinations. Consequently, local governments are exploring strategies such as smart traffic management systems and alternative transportation modes to alleviate congestion and promote sustainability. However, the escalating vehicular influx has led to a surge in traffic accidents and air pollution, posing significant threats to public health and ecological balance. Continual monitoring of road traffic is crucial for preempting traffic bottlenecks, averting accidents, and promoting sustainable transportation practices. Advanced technologies, including unmanned aerial vehicles (UAVs), have emerged as powerful tools for real-time traffic monitoring. Equipped with high-resolution cameras and sensor systems, UAVs facilitate efficient data acquisition from desired regions, offering advantages over traditional aerial vehicles. While UAVs present promising solutions, real-time utilization poses challenges such as complex background scenes and detection misses. Additionally, advancements in object detection algorithms, particularly the You Only Look Once (YOLO) algorithm, have revolutionized vehicle detection. This study focuses on evaluating vehicle detection using YOLOv9, the latest iteration, and compares it with YOLOv7, its predecessor, leveraging aerial monitoring via UAVs. By elucidating the innovations introduced by YOLOv9, this study contributes to enhancing traffic monitoring capabilities and promoting effective transportation management strategies. © 2024 IEEE.
dc.identifier.doi10.1109/RusAutoCon61949.2024.10694108
dc.identifier.endpage155
dc.identifier.issn2836-6131
dc.identifier.issue2024
dc.identifier.scopus2-s2.0-85208230312
dc.identifier.scopusqualityN/A
dc.identifier.startpage150
dc.identifier.urihttps://doi.org/10.1109/RusAutoCon61949.2024.10694108
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1418
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofRusAutoCon - Proceedings of the International Russian Automation Conference
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250316
dc.subjectaerial monitoring
dc.subjectintelligent transportation systems
dc.subjectUAV
dc.subjectvehicle detection
dc.subjectYOLOv7
dc.subjectYOLOv9
dc.titleRefining Transportation Automation with Convolutional Neural Network-Based Vehicle Detection via UAVs
dc.typeConference Object

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