Modelling of Learning and Perception

Centre of Excellence in Computational Complex Systems Research


Monitoring the Condition of Sewer Network

Researchers: Juho Kannala, Jukka Laurila, Sami Brandt, Aki Vehtari and Jouko Lampinen

The aim of the project was to develop methods for the analysis of video sequences that are scanned by a robot moving in the sewer. The project was done in co-operation with the VTT Building and Transport and was funded by TEKES. The work was divided into two parts: 1) Automatic detection of pipe surface defects and pipe joints. 2) Automatic reconstruction of the 3D shape of the pipe.

Displaced joints and surface cracks are among the most common types of defects in a sewer pipe. Detecting the cracks is especially challenging because of the large variation in surface texture. We tested several line detection algorithms for the detection of cracks in the pipe surface and joints between pipe sections. The method illustrated in Figure 1 is based on forming an approximate Hessian for each pixel in the image. The Hessian has a large positive eigenvalue where there is a dark line in the image; these are crack candidates. Post-processing includes thresholding with hysteresis and thresholding by feature size.

Figure 1 shows an example of pipe segment and crack candidates and joints. Post-processing included thresholding with hysteresis, thresholding by feature size and na´ve Bayes classification to combine information from the location and surrounding texture. Approach was was compared to crack detection made by expert. Results showed that approach based on edge line detectors could detect obvious cracks and results were improved compared to previous line detector based method. However, expert could also detect faint cracks based on additional information, like trail left by water dripping through crack and crack continuation on the other side of pipe. Implementation of similar expert knowledge and more holistic approach is not however trivial.

Figure 1a


Figure 1b


Figure 1. Crack detection results. (a) Original image of 'unwrapped' pipe surface. (b) Detected cracks and joints.

The information of the shape of the sewer pipe is important, because the bendings and compressions may indicate upcoming failure. In order to obtain 3D information from the video the imaging geometry of the fish-eye lens camera must be determined. In the project we developed an accurate and easy-to-use method for the calibration of fish-eye lenses. The calibration is possible by using only one view of a planar calibration object as Figure 2 illustrates. After solving the problem of calibration, we were able to use known multiple view techniques to track points through the image sequence and to make 3D reconstruction of the sewer pipe.

Figure 2a


Figure 2b


Figure 2. Fish-eye lens calibration using only one view. (a) Original image. (b) The image corrected to follow the pinhole model. Straight lines are straight as they should be.