Self-supervised learning for multi-label sewer defect classification
Tugba Yildizli (TU Delft - Water Systems Engineering)
Tianlong Jia (TU Delft - Water Systems Engineering, Karlsruhe Institut für Technologie)
Jeroen Langeveld (TU Delft - Water Systems Engineering, Partners4UrbanWater)
Riccardo Taormina (TU Delft - Water Systems Monitoring & Modelling)
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Abstract
Automated sewer defect detection has advanced through deep learning, particularly supervised methods using CCTV images, but based on large annotated datasets. This paper proposes a semi-supervised learning (SSL) approach to reduce labeling demands. The method comprises self-supervised pre-training on unlabeled images using SwAV (Swapping Assignments between multiple Views) followed by fine-tuning for multi-label classification. Experiments on the Sewer-ML dataset demonstrate that the SSL approach, trained on only 35k labeled images, achieves an F1-score of 69.11%, and F2CIW of 54.22%, surpassing the fully supervised baseline trained from scratch on 1.04 million images. Increasing the unlabeled pre-training data further enhances performance, while ImageNet initialization consistently outperforms training from scratch. Self-supervised learning also helps mitigate the effects of mislabeled data, which is observed to be present even in the Sewer-ML ground truth. Overall, self-supervised learning provides an accurate, scalable, and cost-effective alternative to fully supervised approaches, particularly in data-scarce or imperfectly labeled scenarios.
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