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dc.contributor.authorLiu, Chunwu
dc.contributor.authorWang, Zhaokui
dc.date.accessioned2026-08-20T21:12:59Z
dc.date.available2026-08-20T21:12:59Z
dc.date.copyright2026/05/12
dc.date.issued2026
dc.identifier.otherJAEEEZ.ASENG-6165.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314126
dc.description.abstractAbstractAccurate detection of aircraft surface damage is critical for assessing structural integrity, yet the scarcity of labeled damage images poses a significant challenge for deep learning–based inspection methods. This paper proposes a novel ...Practical ApplicationsThis research introduces an advanced aircraft damage detection approach tailored for operational environments where obtaining extensive labeled image data is impractical. By leveraging ADISA-GAN, the framework significantly enhances ...
dc.publisherAmerican Society of Civil Engineers
dc.titleIntelligent Inspection Method for Aircraft Damage Defects Based on Small-Sample Data Augmentation
dc.typeJournal Article
dc.journal.volume39
dc.journal.issue4
dc.journal.titleJournal of Aerospace Engineering
dc.identifier.doi10.1061/JAEEEZ.ASENG-6165
dc.journal.fristpage04026023-1
dc.journal.lastpage04026023-16
dc.page16
dc.treeJournal of Aerospace Engineering:;2026:;Volume ( 039 ):;issue: 004
dc.contenttypeFulltext


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