Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach

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dc.contributor.authorThomas, Julien
dc.contributor.authorKuang, Boyu
dc.contributor.authorWang, Yizhong
dc.contributor.authorBarnes, Stuart
dc.contributor.authorJenkins, Karl
dc.date.accessioned2024-11-28T14:40:29Z
dc.date.available2024-11-28T14:40:29Z
dc.date.freetoread2024-11-28
dc.date.issued2024-12-31
dc.date.pubOnline2024-11-05
dc.description.abstractThe implementation of Smart Airport and Airport 4.0 visions relies on the integration of automation, artificial intelligence, data science, and aviation technology to enhance passenger experiences and operational efficiency. One essential factor in the integration is the semantic segmentation of the aircraft main components (AMC) perception, which is essential to maintenance, repair, and operations in aircraft and airport operations. However, AMC segmentation has challenges from low data availability, high-quality annotation scarcity, and categorical imbalance, which are common in practical applications, including aviation. This study proposes a novel AMC segmentation solution, employing a transfer learning framework based on a sophisticated DeepLabV3 architecture optimized with a custom-designed Focal Dice Loss function. The proposed solution remarkably suppresses the categorical imbalance challenge and increases the dataset variability with manually annotated images and dynamic augmentation strategies to train a robust AMC segmentation model. The model achieved a notable intersection over union of 84.002% and an accuracy of 91.466%, significantly advancing the AMC segmentation performance. These results demonstrate the effectiveness of the proposed AMC segmentation solution in aircraft and airport operation scenarios. This study provides a pioneering solution to the AMC semantic perception problem and contributes a valuable dataset to the community, which is fundamental to future research on aircraft and airport semantic perception.
dc.description.journalNameThe Visual Computer
dc.identifier.citationThomas J, Kuang B, Wang Y, et al., (2024) Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach. The Visual Computer, Available online 5 November 2024
dc.identifier.eissn1432-2315
dc.identifier.elementsID556057
dc.identifier.issn0178-2789
dc.identifier.urihttps://doi.org/10.1007/s00371-024-03686-8
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23233
dc.languageEnglish
dc.language.isoen
dc.publisherSpringer
dc.publisher.urihttps://link.springer.com/article/10.1007/s00371-024-03686-8
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAircraft main component segmentation
dc.subjectTransfer learning
dc.subjectDeepLabV3
dc.subjectFocal dice loss
dc.subjectData augmentation
dc.subjectAviation semantic perception
dc.subject4605 Data Management and Data Science
dc.subject46 Information and Computing Sciences
dc.subjectData Science
dc.subjectSoftware Engineering
dc.subject4607 Graphics, augmented reality and games
dc.titleAdvanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-10-09

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