KrakN: Transfer Learning framework for thin crack detection in infrastructure maintenance.

@article{arski2020KrakNTL,
  title={KrakN: Transfer Learning framework for thin crack detection in infrastructure maintenance.},
  author={Mateusz Żarski and Bartosz W'ojcik and Jaroslaw Adam Miszczak},
  journal={arXiv: Computer Vision and Pattern Recognition},
  year={2020}
}

Datasets and Methods for Boosting Infrastructure Inspection: A Survey on Defect Classification

Based on the established crack dataset, a comprehensive comparison between state-of-the-art algorithms for object classification and segmentation is made, which provides a baseline for future research in defects inspection.

Datasets and Methods for Boosting Infrastructure Inspection: A Survey on Defect Segmentation and Detection

A systematic survey on 26 publicly available datasets for defect segmentation and detection and a comprehensive comparison between existing state-of-the-art algorithms for crack segmentation, which provides a comprehensive baseline for future research in defects inspection.

Finicky transfer learning—A method of pruning convolutional neural networks for cracks classification on edge devices

A novel method of combining the pruning and the transfer learning techniques for the purpose of delivering solid accuracy while simultaneously lowering the demand for energy and computing power is proposed.

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