Image-processing-based automatic crack detection and classification for refractory evaluation

Wang, Guanzheng; Tan, Fangguan; Jin, Shengli; Pan, Liping; He, Zhu; Wang, Xueqing; Li, Yawei; Li, Jing

doi:10.1016/j.ceramint.2022.04.307

Cited by 5 publications

(1 citation statement)

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“…Indirect methods rely on the results of Digital Image and Volume Correlation techniques (DIC/DVC), as shown in Figure 1b. Direct methods typically involve a preprocessing step [1,4,5], such as smoothing, filtering, or contrast enhancement, followed by a detection step, which may use morphological, statistical [6,7] (e.g., Gabor filter bank (GFB), Grey Level Co-occurrence Matrix (GLCM), Local Binary Matrix Feature Extraction (LBFE)), or machine learning methods [1,8,9]. Pixel-intensity-based crack detectors depend on the lighting conditions and require a homogeneous appearance of the uncracked areas [10]; indeed one of the main limitations of the image-based techniques is that the surface noise might be considered a crack [11].…”

Section: Introductionmentioning

confidence: 99%

Image-Based Crack Detection Using Total Variation Strain DVC Regularization

et al. 2023

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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.

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