Boosting Aircraft Monitoring and Security through Ground Surveillance Optimization with YOLOv9

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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 object detection algorithms into aircraft tracking and ground surveillance systems presents a myriad of security advantages, bolstering the protection of critical infrastructure. These algorithms are instrumental in enforcing access control measures by continuously monitoring and discerning between authorized and unauthorized access to parked aircraft. With ongoing refinements, they serve as crucial components of intrusion detection systems, promptly alerting security personnel to any suspicious or unauthorized activities in the vicinity of grounded aircraft. Effective training of detection algorithms enhances their analytical capabilities, enabling them to discern between routine operations and security-threatening situations with greater precision. Notably, one of the pivotal applications lies in supporting digital forensic investigations, as these algorithms provide detailed activity logs, facilitating comprehensive post-incident analyses and bolstering forensic efforts to understand security incidents or breaches. In this investigation, we assessed the efficacy of the YOLOv9 detection algorithm in identifying aircraft situated on the ground surface. Furthermore, we highlight the significance of satellite imagery in dataset acquisition for object detection algorithms, particularly emphasizing the role of Low-Earth-Orbit (LEO) satellites in real-time image acquisition. Through this comprehensive analysis, we underscore the pivotal role of YOLOv9 in enhancing security measures and compliance with aviation security standards and regulations, ultimately fortifying the security posture within aviation environments. © 2024 IEEE.
dc.identifier.doi10.1109/ISDFS60797.2024.10527349
dc.identifier.isbn979-835033036-6
dc.identifier.scopus2-s2.0-85194028083
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/ISDFS60797.2024.10527349
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1411
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.subjectaircraft security
dc.subjectdeep neural network
dc.subjectobject detection
dc.subjectsatellite imagery
dc.subjectYOLOv9
dc.titleBoosting Aircraft Monitoring and Security through Ground Surveillance Optimization with YOLOv9
dc.typeConference Object

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