Single-Stage Deep Learning in Aviation: YOLOv5 for Satellite-Based Aircraft Detection

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dc.contributor.authorDuhter, Melis
dc.contributor.authorSchultheiss, Klaudia
dc.contributor.authorMarek, Hana Marie
dc.contributor.authorStarha, Monika Lenka
dc.contributor.authorKuzu, Hilal
dc.date.accessioned2025-03-17T12:22:49Z
dc.date.available2025-03-17T12:22:49Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024 -- 21 September 2024 through 22 September 2024 -- Malatya -- 203423
dc.description.abstractRecent advancements in object detection technology have revolutionized real-time remote sensing capabilities through satellite imagery. Close-orbit satellites, unmanned aerial vehicles, and autonomous vehicles have emerged as preferred platforms for object detection, offering operational advantages across various applications. Deep learning algorithms play a pivotal role in object detection. Aircraft detection is particularly critical in civil and military aviation contexts, facilitating efficient traffic routing at airports and enhancing national defense strategies. However, satellite-based remote sensing faces challenges such as image resolution limitations, environmental influences, and complexities in distinguishing small, densely packed aircraft in datasets. To address these challenges, deep learning techniques harness neural networks optimized for parallel processing on GPUs, significantly improving object detection accuracy and efficiency. This study focuses on evaluating the YOLOv5 detection algorithm for ground aircraft detection using satellite-based aerial imaging. YOLOv5's single-stage architecture enables real-time detection and classification of objects with enhanced accuracy and efficiency, making it ideal for applications requiring rapid analysis of large-scale aerial images. Leveraging advanced deep learning methodologies, this research assesses YOLOv5's performance in aircraft detection, highlighting its potential in advancing aviation management, surveillance, and security practices. © 2024 IEEE.
dc.identifier.doi10.1109/IDAP64064.2024.10711140
dc.identifier.isbn979-833153149-2
dc.identifier.scopus2-s2.0-85207858585
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP64064.2024.10711140
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1403
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250316
dc.subjectaircraft detection
dc.subjectconvolutional neural networks
dc.subjectdeep learning
dc.subjectYOLOv5
dc.titleSingle-Stage Deep Learning in Aviation: YOLOv5 for Satellite-Based Aircraft Detection
dc.typeConference Object

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