YOLOv9-Enabled Vehicle Detection for Urban Security and Forensics Applications

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dc.contributor.authorBakirci, Murat
dc.contributor.authorBayraktar, Irem
dc.date.accessioned2025-03-17T12:22:49Z
dc.date.available2025-03-17T12:22:49Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description12th International Symposium on Digital Forensics and Security, ISDFS 2024 -- 29 April 2024 through 30 April 2024 -- San Antonio -- 199532
dc.description.abstractThe integration of artificial intelligence (AI) techniques in vehicle detection holds significant promise, particularly in forensic and security domains. Leveraging object detection algorithms enables real-time monitoring of vehicles by competent authorities, aiding in continuous surveillance of roads and highways for various surveillance objectives. Additionally, it streamlines tasks such as identifying stolen vehicles, tracking suspects, and enforcing traffic regulations. Object detection technology also proves invaluable in forensic analysis, aiding criminal investigations and accident reconstructions. Furthermore, it enhances security by detecting aberrant behavior and potential threats at critical infrastructure sites. Concurrently, the remarkable advancements in unmanned aerial vehicles (UAVs) have led to their widespread application across diverse domains, including traffic monitoring and intelligent transportation systems. Equipped with high-resolution cameras, UAVs offer precise imagery for vehicle detection, facilitating swift responses to incidents. This study focuses on vehicle detection from aerial urban transportation images using YOLOv9 on a UAV platform, demonstrating the feasibility and efficacy of aerial analysis for efficient vehicle detection and timely alerts to competent authorities. © 2024 IEEE.
dc.identifier.doi10.1109/ISDFS60797.2024.10527304
dc.identifier.isbn979-835033036-6
dc.identifier.scopus2-s2.0-85194062214
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/ISDFS60797.2024.10527304
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1410
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof12th International Symposium on Digital Forensics and Security, ISDFS 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250316
dc.subjectaerial monitoring
dc.subjectforensic investigation
dc.subjectUAV
dc.subjectvehicle detection
dc.subjectYOLOv9
dc.titleYOLOv9-Enabled Vehicle Detection for Urban Security and Forensics Applications
dc.typeConference Object

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