A texture-Based Video Processing Methodology Using Bayesian Data Fusion for Autonomous Crack Detection on Metallic Surfaces

Regular inspection of the components of nuclear power plants is important to improve their resilience. However, current inspection practices are time consuming, tedious, and subjective: they involve an operator manually locating cracks in metallic surfaces in the plant by watching videos. At the same time, prevalent automatic crack detection algorithms may not detect cracks in metallic surfaces because these are typically very small and have low contrast. Moreover, the existences of scratches, welds, and grindmarks lead 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. The performance of the proposed approach is assessed by using several inspection videos. The results indicate that it is accurate and robust in cases where state-of-the-art crack detection approaches fail. The experiments show that Bayesian data fusion improves the hit rate by 20% and the hit rate achieves 85% with only one false positive per frame.