A Deep Learning Based Classifier for Crack Detection with Robots in Underground Pipes

Underground utility pipes especially sewer pipes are prone to develop cracks due to aging, shifting soil, increased traffic, corrosion, and improper installation. A major challenge for utility operators is cost effective periodic condition monitoring of their sewer networks. The existing industry standard pipe condition monitoring system is based on passing a robot mounted closed circuit television (CCTV) camera through the pipe. The CCTV video feed is recorded and monitored by a trained operator who annotates it corresponding to the location of cracks and other structural imperfections. This system is both cost and labor intensive. In recent years, the deep learning-based systems have achieved success in vision based object detection problems. In this project, we have collected pipe crack data from extensive field trials with CCTV based systems in actual sewer networks. The noisy field data is cleaned up and used for training the convolutional neural networks. We test the proposed model with validation data to determine its accuracy and effectiveness. The results indicate that deep learning model can be effectively used to detect cracks in underground pipes.