Detection of Cracks in Nuclear Power Plant Using Spatial-Temporal Grouping of Local Patches
Abstract
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.
Cite
Text
Schmugge et al. "Detection of Cracks in Nuclear Power Plant Using Spatial-Temporal Grouping of Local Patches." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016. doi:10.1109/WACV.2016.7477601Markdown
[Schmugge et al. "Detection of Cracks in Nuclear Power Plant Using Spatial-Temporal Grouping of Local Patches." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016.](https://mlanthology.org/wacv/2016/schmugge2016wacv-detection/) doi:10.1109/WACV.2016.7477601BibTeX
@inproceedings{schmugge2016wacv-detection,
title = {{Detection of Cracks in Nuclear Power Plant Using Spatial-Temporal Grouping of Local Patches}},
author = {Schmugge, Stephen J. and Rice, Lance and Nguyen, Nhat Rich and Lindberg, John and Grizzi, Robert and Joffe, Chris and Shin, Min C.},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2016},
pages = {1-7},
doi = {10.1109/WACV.2016.7477601},
url = {https://mlanthology.org/wacv/2016/schmugge2016wacv-detection/}
}