Application of machine learning methods on the defect detection in shearographic images

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review


Defect detection in primary composite lightweight structures is a major ongoing challenge in terms of reliability, rapidity, and accuracy for a secure operational life-cycle. Shearography is a full-field and material independent non-destructive testing method. Despite its major suitability for large composite components, this method still requires specialists to reliably identify the defect patterns. Modern algorithms in terms of machine learning have gained huge popularity and provide the ability to outperform conventional algorithms, especially in image analysis. Hence, an object detection model based on convolutional neural networks has been implemented and applied to shearographic images of different composite specimens. Concluding, the model performs with a considerable high accuracy considering the medium sized and manually labeled dataset.


Original languageEnglish
Title of host publicationProceedings of the 20th European Conference on Composite Materials
EditorsAnastasios P. Vassilopoulos, Véronique Michaud
PublisherEcole Polytechnique Fédérale de Lausanne (EPFL)
Number of pages10
ISBN (Electronic)978-2-9701614-0-0
Publication statusPublished - 12 Dec 2022


Title20th European Conference on Composite Materials: Composites Meet Sustainability
SubtitleECCM 2022
Duration26 - 30 June 2022

External IDs

Scopus 85149178017
ORCID /0000-0003-1370-064X/work/142243796
ORCID /0000-0002-6817-1020/work/142246611
ORCID /0000-0003-2653-7546/work/142249396



  • shearography, non-destructive testing, composite, machine learning