Advanced thermal imaging processing and deep learning integration for enhanced defect detection in carbon fiber-reinforced polymer laminates

Date published

2025-03-25

Free to read from

2025-04-28

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Volume Title

Publisher

MDPI

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Type

Article

ISSN

1996-1944

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Citation

Garcia Rosa R, Pereira Barella B, Garcia Vargas I, et al., (2025) Advanced thermal imaging processing and deep learning integration for enhanced defect detection in carbon fiber-reinforced polymer laminates. Materials, Volume 18, Issue 7, March 2025, Article number 1448

Abstract

Carbon fiber-reinforced polymer (CFRP) laminates are widely used in aerospace, automotive, and infrastructure industries due to their high strength-to-weight ratio. However, defect detection in CFRP remains challenging, particularly in low signal-to-noise ratio (SNR) conditions. Conventional segmentation methods often struggle with noise interference and signal variations, leading to reduced detection accuracy. In this study, we evaluate the impact of thermal image preprocessing on improving defect segmentation in CFRP laminates inspected via pulsed thermography. Polynomial approximations and first- and second-order derivatives were applied to refine thermographic signals, enhancing defect visibility and SNR. The U-Net architecture was used to assess segmentation performance on datasets with and without preprocessing. The results demonstrated that preprocessing significantly improved defect detection, achieving an Intersection over Union (IoU) of 95% and an F1-Score of 99%, outperforming approaches without preprocessing. These findings emphasize the importance of preprocessing in enhancing segmentation accuracy and reliability, highlighting its potential for advancing non-destructive testing techniques across various industries.

Description

Software Description

Software Language

Github

Keywords

40 Engineering, 4016 Materials Engineering, 4001 Aerospace Engineering, 34 Chemical sciences, 40 Engineering

DOI

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Attribution 4.0 International

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Funder/s

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001. H.F. gratefully acknowledges the financial support of CNPq (Grants #312530/2023-4 and #407140/2021-2).