UAV-supported visual inspection of aircraft for corrosion and crack detection

ArticleAçık Erişim

Özet

Maintenance procedures are critically important for preserving the structural integrity, maintaining the functionality and ensuring the operational safety of aircraft. Traditional inspection techniques used in aircraft are often costly, time-consuming and prone to human mistake. Today, the opportunities provided by digitalisation and automation in aircraft maintenance and inspection processes are paving the way for innovative approaches. In this context, the use of inspection systems supported by image processing technologies has the potential to bring about a significant transformation in aircraft maintenance. Visual inspection methods integrated with unmanned aerial vehicles (UAVs) enable the rapid, accurate and repeatable detection of defects such as corrosion and cracks on the external surfaces of aircraft. This study focuses on the automatic detection and classification of defects on the external surfaces of aircraft, based on tests and analyses carried out by artificial intelligence algorithms using high-resolution data. The model developed in this study was implemented in Python in the Google Colab environment and supported by AI algorithms trained on visual data. The main objective is to investigate the feasibility of UAV-based systems for aircraft visual inspection and to provide concrete evidence of their practical applicability. In this regard, the UAV platform selected for image acquisition is intended to comprehensively scan the target areas and capture images with sufficient resolution for processing by artificial intelligence algorithms. A review of the literature reveals that UAV- and AI-based integrated approaches have been explored in only a limited number of studies related to aircraft maintenance. In this context, the present study proposes a system that enables the rapid and accurate detection of structural defects such as corrosion and cracks on the external surfaces of aircraft. © The Author(s), 2026. Published by Cambridge University Press on behalf of Royal Aeronautical Society.

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RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/ebyu/10132Resmi atıflarda kullanılan kalıcı adres
DOI
https://doi.org/10.1017/aer.2026.10136Yayıncı resmi DOI adresi
Tam Kayıt
AlanDeğerDil
dc.contributor.authorKurt, B.
dc.contributor.authorSoylak, M.
dc.contributor.authorKöse, O.
dc.date.accessioned2026-10-18T20:25:23.671349907Z
dc.date.available2026-10-18T20:25:23.671349907Z
dc.date.issued2026
dc.description.abstractMaintenance procedures are critically important for preserving the structural integrity, maintaining the functionality and ensuring the operational safety of aircraft. Traditional inspection techniques used in aircraft are often costly, time-consuming and prone to human mistake. Today, the opportunities provided by digitalisation and automation in aircraft maintenance and inspection processes are paving the way for innovative approaches. In this context, the use of inspection systems supported by image processing technologies has the potential to bring about a significant transformation in aircraft maintenance. Visual inspection methods integrated with unmanned aerial vehicles (UAVs) enable the rapid, accurate and repeatable detection of defects such as corrosion and cracks on the external surfaces of aircraft. This study focuses on the automatic detection and classification of defects on the external surfaces of aircraft, based on tests and analyses carried out by artificial intelligence algorithms using high-resolution data. The model developed in this study was implemented in Python in the Google Colab environment and supported by AI algorithms trained on visual data. The main objective is to investigate the feasibility of UAV-based systems for aircraft visual inspection and to provide concrete evidence of their practical applicability. In this regard, the UAV platform selected for image acquisition is intended to comprehensively scan the target areas and capture images with sufficient resolution for processing by artificial intelligence algorithms. A review of the literature reveals that UAV- and AI-based integrated approaches have been explored in only a limited number of studies related to aircraft maintenance. In this context, the present study proposes a system that enables the rapid and accurate detection of structural defects such as corrosion and cracks on the external surfaces of aircraft. © The Author(s), 2026. Published by Cambridge University Press on behalf of Royal Aeronautical Society.
dc.description.sponsorshipThis study was supported by Erciyes University Scientific Research Projects Coordination. The unit is under grant number code FDK-2025-14533. The testing and analysis stages were carried out using financial resources provided under this project.
dc.identifier.doi10.1017/aer.2026.10136
dc.identifier.eissn2059-6464
dc.identifier.issn0001-9240
dc.identifier.scopus2-s2.0-105029662128
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/ebyu/10132
dc.identifier.wosWOS:001677373600001
dc.language.isoen
dc.publisherCambridge University Press
dc.relation.ispartofAeronautical Journal
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectaircraft visual inspection
dc.subjectdefect detection with UAV
dc.subjectimage processing
dc.subjectunmanned aerial vehicles
dc.subjectAircraft detection
dc.subjectAntennas
dc.subjectCorrosion
dc.subjectFighter aircraft
dc.subjectImage acquisition
dc.subjectInspection
dc.subjectMaintenance
dc.subjectStructural analysis
dc.subjectSurface defects
dc.subjectAerial vehicle
dc.subjectAircraft maintenance
dc.subjectDefect detection
dc.subjectDefect detection with unmanned aerial vehicle
dc.subjectExternal surfaces
dc.subjectImages processing
dc.subjectUnmanned aerial vehicle
dc.subjectVisual inspection
dc.subjectUnmanned aerial vehicles (UAV)
dc.titleUAV-supported visual inspection of aircraft for corrosion and crack detection
dc.typeArticle
dspace.entity.typePublication
rdl.citation.scopus1
rdl.citation.wos1
rdl.source.indexSCOPUS
rdl.source.indexWOS
rdl.wos.indexScience Citation Index Expanded (SCI-EXPANDED)