Machine learning and computer vision for urban drainage inspections

Sewer pipes are an essential infrastructure in modern society and their proper operation is important for public health. To keep sewer pipes operational as much as possible, periodical inspections for defects are performed. Instead of repairing sewer pipes when a problem becomes critical, such inspections allow municipalities to plan maintenance.Sewer pipe inspections are an attractive target for automation. While a potential improvement in terms of assessment quality and processing efficiency is generally promised by automation, in this case we would also decrease the variability which is a current problem. Besides the reasons for automating, the methods for automating are also attractive: a lot of (visual) data has been gathered over the past decades which may be used to train algorithms.This thesis compiles the results of five years of research into the possible automation of sewer pipe inspections with the tools of machine learning and computer vision. In this thesis, three distinct, yet complementary approaches to automating sewer pipe inspections are described:- Image-Based Unsupervised Anomaly Detection- Convolutional Neural Network Classification- Stereovision and Geometry Reconstruction

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