Aircraft skin machine learning-based defect detection and size estimation in visual inspections

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dc.contributor.authorPlastropoulos, Angelos
dc.contributor.authorBardis, Kostas
dc.contributor.authorYazigi, George
dc.contributor.authorAvdelidis, Nicolas P.
dc.contributor.authorDroznika, Mark
dc.date.accessioned2024-10-21T14:34:53Z
dc.date.available2024-10-21T14:34:53Z
dc.date.freetoread2024-10-21
dc.date.issued2024-09-10
dc.date.pubOnline2024-09-10
dc.description.abstractAircraft maintenance is a complex process that requires a highly trained, qualified, and experienced team. The most frequent task in this process is the visual inspection of the airframe structure and engine for surface and sub-surface cracks, impact damage, corrosion, and other irregularities. Automated defect detection is a valuable tool for maintenance engineers to ensure safety and condition monitoring. The proposed approach is to process the captured feedback using various deep learning architectures to achieve the highest performance defect detections. Additionally, an algorithm is proposed to estimate the size of the detected defect. The team collaborated with TUI’s Airline Maintenance Team at Luton Airport, allowing us to fly a drone inside the hangar and use handheld cameras to collect representative data from their aircraft fleet. After a comprehensive dataset was constructed, multiple deep-learning architectures were developed and evaluated. The models were optimized for detecting various aircraft skin defects, with a focus on the challenging task of dent detection. The size estimation approach was evaluated in both controlled laboratory conditions and real-world hangar environments, providing insights into practical implementation challenges.
dc.description.journalNameTechnologies
dc.description.sponsorshipEngineering and Physical Sciences Research Council, Research England, Boeing (United States)
dc.description.sponsorshipThis research was supported and funded by the British Engineering and Physics Sciences Research Council (EPSRC IAA project) and Boeing Company.
dc.format.extentpp. 158-158
dc.identifier.citationPlastropoulos A, Bardis K, Yazigi G, et al., (2024) Aircraft skin machine learning-based defect detection and size estimation in visual inspections. Technologies, Volume 12, Issue 9, September 2024, Article number 158
dc.identifier.eissn2227-7080
dc.identifier.elementsID553283
dc.identifier.issn2227-7080
dc.identifier.issueNo9
dc.identifier.paperNoARTN 158
dc.identifier.urihttps://doi.org/10.3390/technologies12090158
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23052
dc.identifier.volumeNo12
dc.languageEnglish
dc.language.isoen
dc.publisherMDPI
dc.publisher.urihttps://www.mdpi.com/2227-7080/12/9/158
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectdefect detection
dc.subjectdefect estimation
dc.subjectaircraft inspection
dc.subjectunmanned aerial vehicles
dc.subjectdeep learning
dc.subjectUAV
dc.subjectvisual checks
dc.subjectaircraft maintenance
dc.subject40 Engineering
dc.subject4008 Electrical Engineering
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.subjectMachine Learning and Artificial Intelligence
dc.subject4008 Electrical engineering
dc.titleAircraft skin machine learning-based defect detection and size estimation in visual inspections
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-09-05

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