Smart Informatix Laboratory

Home

Texture-based Video Processing Using Bayesian Data Fusion for Crack Detection

Regular inspection of the components of nuclear power plants is important to improve their resilience. Prevalent automatic crack detection algorithms may not detect cracks in metallic surfaces because these are typically very small and have low contrast. Moreover, the existence of scratches, welds, and grind marks leads to a large number of false positives when state-of-the-art vision-based crack detection algorithms are used. In this study, a novel crack detection approach is proposed based on local binary patterns (LBP), support vector machine (SVM), and Bayesian decision theory. The proposed method aggregates the information obtained from different video frames to enhance the robustness and reliability of detection.

Related Publication

Fu-Chen Chen, Mohammad R. Jahanshahi, Rih-Teng Wu and Chris Joffe, (2017), "A texture-based video processing methodology using Bayesian data fusion for autonomous crack detection on metallic surfaces," Computer-Aided Civil and Infrastructure Engineering, Vol. 32, No. 4, April 2017, 271-287, DOI: 10.1111/mice.12256.publications.html.