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

Abstract

This review provides a state-of-the-art survey of image-based automation techniques for sewer inspection, focusing on the progression from traditional rule-based image processing to advanced deep learning models. The core computer vision tasks are examined, including defect classification, object detection, and semantic segmentation, with an emphasis on their capabilities and limitations. Recent trends, such as the use of multilabel classification to detect multiple defects in a single image and transformer-based models for better understanding of visual context, are also discussed. The review also explores the emerging role of three-dimensional (3D) point cloud analysis and 3D reconstruction techniques in enhancing spatial understanding and defect localization. Despite notable advances, several challenges remain, such as the scarcity of standardized datasets, limited real-world validation, and the absence of fully automated deployable systems. To address these gaps, the paper recommends promoting open-access benchmark datasets; encouraging the open sharing of code and models; the adoption of data-efficient learning approaches such as unsupervised, semisupervised, self-supervised, and transfer learning to reduce reliance on large annotated datasets; the use of lightweight and deployable model architectures; and the validation of AI-driven models in real-world sewer inspection environments. These directions are essential to transforming current research innovations into practical, intelligent inspection systems.