Self-Supervised Learning for Identifying Defects in Sewer Footage
<|reference_start|>Self-Supervised Learning for Identifying Defects in Sewer Footage: Sewerage infrastructure is among the most expensive modern investments requiring time-intensive manual inspections by qualified personnel. Our study addresses the need for automated solutions without relying on large amounts of labeled data. We propose a novel application of Self-Supervised Learning (SSL) for sewer inspection that offers a scalable and cost-effective solution for defect detection. We achieve competitive results with a model that is at least 5 times smaller than other approaches found in the literature and obtain competitive performance with 10\% of the available data when training with a larger architecture. Our findings highlight the potential of SSL to revolutionize sewer maintenance in resource-limited settings.<|reference_end|>
@article{otero2024self-supervised,
    title={Self-Supervised Learning for Identifying Defects in Sewer Footage},
    author={Daniel Otero and Rafael Mateus},
    journal={arXiv preprint arXiv:2409.02140},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02140},
    primaryClass={cs.CV cs.AI cs.LG}
}