mlsanityData to 7 Oct 2026

Self-Supervised Learning for Identifying Defects in Sewer Footage

arXiv 2409.02140 ↗First posted 2 Sep 2024cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 28 references that are in the map. First posted on arXiv 2 Sep 2024.

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19 of 28 references.

References · 19

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7 more
  1. 2021Solo-learn: A Library of Self-supervised Methods for Visual Representation Learningcited by 116
  2. 2019Multi-Label Classification with Label Graph Superimposingcited by 65
  3. 2019Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learningcited by 25
  4. 2021Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmarkcited by 17
  5. 2022Towards Trustworthy Multi-label Sewer Defect Classification via Evidential Deep Learningcited by 5
  6. 2021Multi-Task Classification of Sewer Pipe Defects and Properties using a Cross-Task Graph Neural Network Decodercited by 3
  7. 2022Effective Self-supervised Pre-training on Low-compute Networks without Distillationcited by 3

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