Pipe inspection using a laser-based transducer and automated analysis techniques

This paper presents a new sensing methodology for the automated inspection of pipes. Standard inspection systems, as they are for example used in waste pipes and drains, are based on closed-circuit television cameras which are mounted on remotely controlled platforms and connected to remote video recording facilities. Two of the main disadvantages of such camera-based inspection systems are: 1) the poor quality of the acquired images due to difficult lighting conditions and 2) the susceptibility to error during the offline video assessment conducted by human operators. The objective of this research is to overcome these disadvantages and to create an intelligent sensing approach for improved and automated pipe-condition assessment. This approach makes use of a low-cost lighting profiler and a camera which acquires images of the light projections on the pipe wall. A novel method for extracting and analyzing intensity variations in the acquired images is introduced. The image data analysis is based on differential processing leading to highly-noise tolerant algorithms, particularly well suited for the detection of small faults in harsh environments. With the subsequent application of artificial neural networks, the system is capable of recognizing defective areas with a high success rate. Experiments in a range of waste pipes with different diameters and material properties have been conducted and test results are presented.

[1]  Kaspar Althoefer,et al.  State of the art in sensor technologies for sewer inspection , 2002 .

[2]  Wenwei Zhang,et al.  Noncontact laser sensor for pipe inner wall inspection , 1998 .

[3]  Kaspar Althoefer,et al.  Pipe inspection using intelligent analysis techniques with high noise-tolerance , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  R. Halír.  Numerically Stable Direct Least Squares Fitting of Ellipses , 1998 .

[5]  Tim Ellis,et al.  A semi-autonomous sewer surveillance and inspection vehicle , 1996, Proceedings of Conference on Intelligent Vehicles.

[6]  M. Alonso,et al.  Fundamental University Physics , 1967 .

[7]  Osama Moselhi,et al.  Classification of Defects in Sewer Pipes Using Neural Networks , 2000 .

[8]  John Mashford,et al.  PIRAT—A System for Quantitative Sewer Pipe Assessment , 2000, Int. J. Robotics Res..

[9]  Fakhri Karray,et al.  Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm , 2002, IEEE Trans. Neural Networks.

[10]  Kaspar Althoefer,et al.  Automated sewer inspection using image processing and a neural classifier , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[11]  Kaspar Althoefer,et al.  Automated sewer pipe inspection through image processing , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[12]  Ning Li,et al.  Retrieving Camera Parameters from Real Video Images , 1998 .

[13]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[14]  Gregory Baratoff,et al.  3D interpretation of sewer circular structures , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[15]  Long Quan,et al.  Conic Reconstruction and Correspondence From Two Views , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[17]  M. Hartrumpf,et al.  Optical three-dimensional measurements by radially symmetric structured light projection. , 1997, Applied optics.

[18]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Kaspar Althoefer,et al.  Sewer defect detection and classification using a neural network , 2000 .

[20]  A R Luxmoore,et al.  A pipe-profiling adapter for CCTV inspection cameras: development of a pipe-profiling instrument , 1996 .