Image-Based Crack Detection Using Total Variation Strain DVC Regularization

Author:

Manigrasso Zaira1,Goethals Wannes23ORCID,Kibleur Pierre34ORCID,Boone Matthieu N.23ORCID,Philips Wilfried1,Aelterman Jan123

Affiliation:

1. Department of Telecommunications and Information Processing (TELIN)-IPI Research Group, Faculty of Engineering and Architecture, Ghent University-Imec, Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium

2. Department of Physics and Astronomy, Radiation Physics, Ghent University, Proeftuinstraat 86/N12, B-9000 Ghent, Belgium

3. Centre for X-ray Tomography (UGCT), Ghent University, Proeftuinstraat 86, B-9000 Ghent, Belgium

4. Department of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium

Abstract

Introduction: Accurately detecting cracks is crucial for assessing the health of materials. Manual detection methods are time-consuming, leading to the development of automatic detection techniques based on image processing and machine learning. These methods utilize morphological image processing and material deformation analysis through Digital Image or Volume Correlation techniques (DIC/DVC) to identify cracks. The strain field derived from DIC/DVC tends to be noisy. Traditional denoising methods sacrifice spatial resolution, limiting their effectiveness in capturing abrupt structural deformations such as fractures. Method: In this study, a novel DVC regularization method is proposed to obtain a sharper and less noisy strain field. The method minimizes the total variation of spatial strain field components based on the assumption of approximate strain constancy within material phases. Results: The proposed methodology is validated using simulated data and actual 4D μ-CT experimental data. Compared to classical denoising methods, the proposed DVC regularization method provides a more reliable crack detection with fewer false positives. Conclusions: These results highlight the possibility of estimating a low-noise strain field without relying on the spatial smoothness assumption, thereby improving accuracy and reliability in crack detection.

Funder

Research Foundation-Flanders FWO Strategic Basic Research Program MoCCha-CT

Junior Research Project program

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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