A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation

Autor:	Katsamenis, Iason, Protopapadakis, Eftychios, Bakalos, Nikolaos, Doulamis, Anastasios, Doulamis, Nikolaos, Voulodimos, Athanasios
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing 68T07 (Primary) 68T45 (Secondary) I.2.10 I.4.6
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages, 4 figures, 2 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.01582 Zobrazit plný text záznamu View this record from Arxiv