2023/03/02 by Iason Katsamenis, Katsamenis, Iason, Eftychios Protopapadakis +9 · 2 citations
Engineering · #68T07 (Primary) 68T45 (Secondary) #Asphalt Pavement Performance Evaluation #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #I.2.10 #I.4.6 #Image and Video Processing (eess.IV) #Infrastructure Maintenance and Monitoring #Machine Learning (cs.LG) #Non-Destructive Testing Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2303.01582
openalex publication_date 2023/03/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.