DFS-based fast crack detection
Duc Nguyen, Vsevolod Chernyshev, Vitalii Makogin, Evgeny Spodarev
TL;DR
This paper introduces a rapid crack detection method in 3D CT images using a combination of the Maximal Hessian Entry filter and DFS algorithm, achieving a good balance between speed and accuracy.
Contribution
The paper presents a novel approach combining Hessian filtering and DFS for efficient crack detection in 3D CT scans.
Findings
Effective crack detection with controlled misclassification probability
Fast processing suitable for large 3D datasets
Balanced accuracy and computational efficiency
Abstract
In this paper, we propose a fast method for crack detection in 3D computed tomography (CT) images. Our approach combines the Maximal Hessian Entry filter and a Deep-First Search algorithm-based technique to strike a balance between computational complexity and accuracy. Experimental results demonstrate the effectiveness of our approach in detecting the crack structure with predefined misclassification probability.
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Taxonomy
TopicsNon-Destructive Testing Techniques · Industrial Vision Systems and Defect Detection · Integrated Circuits and Semiconductor Failure Analysis