A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation
Recent studies indicate that deep learning plays a crucial role in the automated visual inspection of road infrastructures. However, current learning schemes are static, implying no dynamic adaptation to users' feedback. To address this drawback, we present a few-shot learning paradigm for the...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
02.03.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Recent studies indicate that deep learning plays a crucial role in the
automated visual inspection of road infrastructures. However, current learning
schemes are static, implying no dynamic adaptation to users' feedback. To
address this drawback, we present a few-shot learning paradigm for the
automated segmentation of road cracks, which is based on a U-Net architecture
with recurrent residual and attention modules (R2AU-Net). The retraining
strategy dynamically fine-tunes the weights of the U-Net as a few new rectified
samples are being fed into the classifier. Extensive experiments show that the
proposed few-shot R2AU-Net framework outperforms other state-of-the-art
networks in terms of Dice and IoU metrics, on a new dataset, named CrackMap,
which is made publicly available at https://github.com/ikatsamenis/CrackMap. |
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DOI: | 10.48550/arxiv.2303.01582 |