In this work, we present an automated inspection approach to assist remote visual examinations of nuclear power plant components. An automated approach would require detecting often low contrast cracks that could be surrounded by or even within textures with similar appearances such as welding, scratches, and grind marks. We propose a crack segmentation method for remote visual examination videos by aggregating the pixel-level classification confidence from multiple frames consisting of different illumination conditions. A dataset of 685 pixel-level ground truth images consisting of 37 cracks from remote visual examination videos is used for evaluation. The results show that the proposed method provides a significant improvement over hand-crafted feature based segmentation and 9% over convolutional neural network based method.

Robust inspection is important to ensure the safety of nuclear power plant components. An automated approach would require detecting often low contrast cracks that could be surrounded by or even within textures with similar appearances such as welding, scratches and grind marks. We propose a crack detection method for nuclear power plant inspection videos by fine tuning a deep neural network for detecting local patches containing cracks which are then grouped in spatial-temporal space for group-level classification. We evaluate the proposed method on a data set consisting of 17 videos consisting of nearly 150,000 frames of inspection video and provide comparison to prior methods.