Video-based crack detection using deep learning and Nave Bayes data fusion

Abstract

Regularly inspecting the components inside nuclear power plants is necessary to ensure their safe performance. Current inspection practices, however, are time consuming, tedious, and subjective that need technicians to watch videos and manually annotate cracks. While an autonomous inspection approach is desirable, state-of-the-art crack detection algorithms cannot perform well since the cracks on nuclear power plant reactors are typically very tiny with low contrast. The existence of scratches, welds, and grind marks on the surface makes the autonomous detection even more challenging. This study introduces a new framework that consists of a convolutional neural network (CNN) based on deep learning, a spatiotemporal registration process, and a Nave Bayes data fusion scheme based on statistics. The proposed framework is evaluated using several inspection videos and achieves 98.3% hit rate against 0.1 false positives per frame where the hit rate is much higher than other state-of-the-art algorithms.