Paper
4 December 2024 Nondestructive testing application of Cessna 172R aircraft skin coating based on spectral indices
Kaige Li, Zhipeng Wu, Youquan Dan, Peiyu Sun, Qingsong Liu, Shenlan Tang, Luopeng Xu
Author Affiliations +
Proceedings Volume 13283, Conference on Spectral Technology and Applications (CSTA 2024); 132834W (2024) https://doi.org/10.1117/12.3037308
Event: Conference on Spectral Technology and Applications (CSTA 2024), 2024, Dalian, China
Abstract
Damage to the skin coating of the Cessna 172R aircraft is an unavoidable and significant issue due to long flight training. Traditional detection methods are easy to missed and false detections, and hyperspectral technology can significantly improve. The spectral curves of the damaged and undamaged skin coating pixels in the near-infrared band (900-1700 nm) of the Cessna 172R aircraft skin samples were used to establish three spectral indices: ASCI-I, ASCI-II, and ASCI-III. Then the decision tree is used to perform recognition experiments on the two types of skin samples. The experimental results show that the decision tree model based on ASCI-I has the best recognition performance. Its global image recognition results are closely related to the spatial distribution of the actual targets. The Producer accuracy, User accuracy, and overall classification accuracy are all over 90%, and the Kappa coefficient is greater than 0.88.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Kaige Li, Zhipeng Wu, Youquan Dan, Peiyu Sun, Qingsong Liu, Shenlan Tang, and Luopeng Xu "Nondestructive testing application of Cessna 172R aircraft skin coating based on spectral indices", Proc. SPIE 13283, Conference on Spectral Technology and Applications (CSTA 2024), 132834W (4 December 2024); https://doi.org/10.1117/12.3037308
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KEYWORDS
Skin

Coating

Performance modeling

Data modeling

Reflectivity

Image classification

Statistical modeling

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