Sewer Defect

Sewer defect detection is a critical area of research focusing on automating the inspection and assessment of sewer pipe conditions to improve efficiency and reduce costs associated with manual inspections. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks, vision transformers, and graph neural networks, often incorporating self-supervised learning techniques to address data scarcity and class imbalance issues. These advancements aim to improve the accuracy and speed of defect identification, classification, and even prediction of future failures, leading to more effective maintenance strategies and optimized resource allocation for sewer infrastructure management. The development of robust and efficient models for sewer defect detection has significant implications for improving public health and safety, as well as reducing economic burdens associated with sewer system maintenance.