Publication Abstracts
Asadi Shamsabadi et al. 2024
Asadi Shamsabadi, E., , C. Xu, and D. Dias-da-Costa, 2024: Efficient semi-supervised surface crack segmentation with small datasets based on consistency regularisation and pseudo-labelling. Autom. Constr., 158, 105181, doi:10.1016/j.autcon.2023.105181.
Despite promising results in vision-based surface crack detection, data-driven approaches still suffer from the scarcity of rich labelled datasets. Such a limitation has hindered a wider practical application of detection models. To address this issue, a semi-supervised framework is proposed, capable of learning from a substantial amount of unlabelled data and achieving high accuracy, even when the available labelled datasets are of limited size. The framework is designed by tailoring supervised training, semi-supervised consistency regularisation, and self-training with certainty-based pseudo-labelling, resulting in a simple yet effective approach. Despite using only 2% of the total labelled Concrete and Asphalt datasets, the resulting mIoU was only 2.6% and 4.7%, respectively, lower than the best performance of the model trained on 100% of the labelled data. Remarkably, the designed framework assisted the model in approaching and even exceeding saturation levels with as little as 20% and 25% of the Concrete and Asphalt datasets.
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BibTeX Citation
@article{as00100k,
author={Asadi Shamsabadi, E. and Erfani, S. M. H. and Xu, C. and Dias-da-Costa, D.},
title={Efficient semi-supervised surface crack segmentation with small datasets based on consistency regularisation and pseudo-labelling},
year={2024},
journal={Automation in Construction},
volume={158},
pages={105181},
doi={10.1016/j.autcon.2023.105181},
}
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RIS Citation
TY - JOUR ID - as00100k AU - Asadi Shamsabadi, E. AU - Erfani, S. M. H. AU - Xu, C. AU - Dias-da-Costa, D. PY - 2024 TI - Efficient semi-supervised surface crack segmentation with small datasets based on consistency regularisation and pseudo-labelling JA - Autom. Constr. JO - Automation in Construction VL - 158 SP - 105181 DO - 10.1016/j.autcon.2023.105181 ER -
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