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.
@article{arxiv.1912.05222,
title = {Automatic Analysis of Sewer Pipes Based on Unrolled Monocular Fisheye Images},
author = {Johannes Künzel and Thomas Werner and Ronja Möller and Peter Eisert and Jan Waschnewski and Ralf Hilpert},
journal= {arXiv preprint arXiv:1912.05222},
year = {2019}
}
Comments
Published in: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV)