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

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dc.contributor.authorGarcia Rosa, Renan
dc.contributor.authorPereira Barella, Bruno
dc.contributor.authorGarcia Vargas, Iago
dc.contributor.authorTarpani, José Ricardo
dc.contributor.authorHerrmann, Hans-Georg
dc.contributor.authorFernandes, Henrique
dc.date.accessioned2025-04-28T09:48:11Z
dc.date.available2025-04-28T09:48:11Z
dc.date.freetoread2025-04-28
dc.date.issued2025-03-25
dc.date.pubOnline2025-03-25
dc.description.abstractCarbon 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.
dc.description.journalNameMaterials
dc.description.sponsorshipThis 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).
dc.identifier.citationGarcia 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
dc.identifier.eissn1996-1944
dc.identifier.elementsID567419
dc.identifier.issn1996-1944
dc.identifier.issueNo7
dc.identifier.paperNo1448
dc.identifier.urihttps://doi.org/10.3390/ma18071448
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23803
dc.identifier.volumeNo18
dc.languageEnglish
dc.language.isoen
dc.publisherMDPI
dc.publisher.urihttps://www.mdpi.com/1996-1944/18/7/1448
dc.relation.isreferencedbydoi:10.17632/jrsb4b9yy5.1
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject40 Engineering
dc.subject4016 Materials Engineering
dc.subject4001 Aerospace Engineering
dc.subject34 Chemical sciences
dc.subject40 Engineering
dc.titleAdvanced thermal imaging processing and deep learning integration for enhanced defect detection in carbon fiber-reinforced polymer laminates
dc.typeArticle
dcterms.dateAccepted2025-03-22

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