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July 31, 2026Journal of Pipeline Systems Engineering and Practice

Advances in Deep Learning and Computer Vision for Sewer Inspection: A State-of-the-Art Review

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Authors

NTNimee TiwariDSDima ShammoutHJHiman Hojat Jalali

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Overview

Review examines image automation techniques for sewer inspection, highlighting innovations and challenges.

Key Points

  • The review aims to survey advancements in image-based automation for sewer inspection, focusing on deep learning technologies.
  • Conducted a review of current literature on image processing techniques for sewer inspection.
  • Analyzed core tasks including classification, detection, and segmentation in various deep learning models.
  • Discussed trends in 3D point cloud analysis and reconstruction for improved defect localization.
  • Identified the potential of deep learning models over traditional methods in defect detection and classification.
  • Noted challenges including lack of standardized datasets and limited real-world validation for existing models.
  • Recommended strategies such as open-access datasets and data-efficient learning to enhance deployment in real sewer systems.

Cite This Study

Tiwari et al. (2026) studied this question.

synapsesocial.com/papers/6a6c4702747664a1aa73c14bhttps://doi.org/10.1061/jpsea2.pseng-2258
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