Automatic Analysis of Sewer Pipes Based on Unrolled Monocular Fisheye Images

Abstract

The task of detecting and classifying damages in sewer pipes offers an important application area for computer vision algorithms. This paper describes a system, which is capable of accomplishing this task solely based on low quality and severely compressed fisheye images from a pipe inspection robot. Relying on robust image features, we estimate camera poses, model the image lighting, and exploit this information to generate high quality cylindrical unwraps of the pipes' surfaces.Based on the generated images, we apply semantic labeling based on deep convolutional neural networks to detect and classify defects as well as structural elements.

Published in: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV)

Cited by in corpus (4)

• From Explanations to Segmentation: Using Explainable AI for Image Segmentation
• Automatic Defect Detection in Sewer Network Using Deep Learning Based Object Detector
• System for 3D Acquisition and 3D Reconstruction using Structured Light for Sewer Line Inspection
• 3D Pipe Network Reconstruction Based on Structure from Motion with Incremental Conic Shape Detection and Cylindrical Constraint