mlsanityData to 7 Oct 2026

Nondestructive Testing of Composite Fibre Materials with Hyperspectral Imaging : Evaluative Studies in the EU H2020 FibreEUse Project

arXiv 2111.03443 ↗First posted 4 Nov 2021cs.CVcited by 3

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Abstract (arXiv)

Through capturing spectral data from a wide frequency range along with the spatial information, hyperspectral imaging (HSI) can detect minor differences in terms of temperature, moisture and chemical composition. Therefore, HSI has been successfully applied in various applications, including remote sensing for security and defense, precision agriculture for vegetation and crop monitoring, food/drink, and pharmaceuticals quality control. However, for condition monitoring and damage detection in carbon fibre reinforced polymer (CFRP), the use of HSI is a relatively untouched area, as existing non-destructive testing (NDT) techniques focus mainly on delivering information about physical integrity of structures but not on material composition. To this end, HSI can provide a unique way to tackle this challenge. In this paper, with the use of a near-infrared HSI camera, applications of HSI for the non-destructive inspection of CFRP products are introduced, taking the EU H2020 FibreEUse project as the background. Technical challenges and solutions on three case studies are presented in detail, including adhesive residues detection, surface damage detection and Cobot based automated inspection. Experimental results have fully demonstrated the great potential of HSI and related vision techniques for NDT of CFRP, especially the potential to satisfy the industrial manufacturing environment.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 2 of its 31 references that are in the map. First posted on arXiv 4 Nov 2021.

Builds on

2 of 31 references.

References · 2

  1. 2019Background Subtraction in Real Applications: Challenges, Current Models and Future Directionscited by 29
  2. 2017Sparse Representation Based Augmented Multinomial Logistic Extreme Learning Machine with Weighted Composite Features for Spectral Spatial Hyperspectral Image Classificationcited by 1

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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2.

3 papers.