Damage Detection in Composite Materials Using Hyperspectral Imaging

Długosz, Jan; Dao, Phong B.; Staszewski, Wiesław J.; Uhl, T.

doi:10.1007/978-3-031-07258-1_48

Cited by 1 publication

(1 citation statement)

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“…One of the most promising advances in SHM and damage detection was made in the field of vision-based methods [ 14 ]. Some of the techniques used in vision-based methods include stereoscopic vision, digital image correlation, computational intelligence-based algorithms and hyperspectral imaging (HSI) [ 15 , 16 ]. Overall, the quality of results obtained using vision-based methods is dependent on the quality of lighting [ 17 ], which is understandably hard to control in real-life applications due to the environmental influences.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection in Glass Fibre Composites Using Cointegrated Hyperspectral Images

Długosz,

Dao,

Staszewski

et al. 2024

Sensors

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Hyperspectral imaging (HSI) is a remote sensing technique that has been successfully applied for the task of damage detection in glass fibre-reinforced plastic (GFRP) materials. Similarly to other vision-based detection methods, one of the drawbacks of HSI is its susceptibility to the lighting conditions during the imaging, which is a serious issue for gathering hyperspectral data in real-life scenarios. In this study, a data conditioning procedure is proposed for improving the results of damage detection with various classifiers. The developed procedure is based on the concept of signal stationarity and cointegration analysis, and achieves its goal by performing the detection and removal of the non-stationary trends in hyperspectral images caused by imperfect lighting. To evaluate the effectiveness of the proposed method, two damage detection tests have been performed on a damaged GFRP specimen: one using the proposed method, and one using an established damage detection workflow, based on the works of other authors. Application of the proposed procedure in the processing of a hyperspectral image of a damaged GFRP specimen resulted in significantly improved accuracy, sensitivity, and F-score, independently of the type of classifier used.

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