Real-time Laser Monitoring based on Pipe Detective Operation
The pipe inspection operation is the difficult detective
performance. Almost applications are mainly relies on a manual
recognition of defective areas that have carried out detection by an
engineer. Therefore, an automation process task becomes a necessary
in order to avoid the cost incurred in such a manual process. An
automated monitoring method to obtain a complete picture of the
sewer condition is proposed in this work. The focus of the research is
the automated identification and classification of discontinuities in
the internal surface of the pipe. The methodology consists of several
processing stages including image segmentation into the potential
defect regions and geometrical characteristic features. Automatic
recognition and classification of pipe defects are carried out by means
of using an artificial neural network technique (ANN) based on
Radial Basic Function (RBF). Experiments in a realistic environment
have been conducted and results are presented.
[1] Campbell, G. Rogers, K. Gilbert, J. Pirat - a system for quantitative
sewer assessment, Interna¬tional No Dig-95, Dresden, Germany, 1995.
[2] Fitzgibbon, A. Pilu, M. Fisher R. Direct Least Square Fitting of Ellipses.
Pattern analysis and machine intelligence, Vol. 21 Issue 5, pp 476-480,
1999.
[3] Gonzalez R.C. Digital Image Processing. Addison-Wesley, MA, 1987.
[4] Halir, R. Flusser J.: Numerically stable direct least squares fitting of
ellipses. The Sixth International Conference in Central Europe on
Computer Graphics and Visualization, Plze├▓, pp. 125-132, 1998.
[5] Moselhi, O. Shehab-Eldeen, T. Automated detection of surface defects
in water and sewer pipes. Automation in Construction 8, pp 581-588,
1999.
[6] Pace, NG. Ultrasonic surveying of fully charged sewage pipes,
Electronics and Communications Engineering Journal, pp 87-92, 1994.
[7] Romero, A. Applications and benefits of using camera technology to
internally inspect polyethylene main service piping", American Gas
Association Operations Conference, Clevehand, Ohio, USA, May 1999.
[8] Roth, H, Schilling, K. Navigation and Control for Pipe Inspection and
Repair Robots: Proc of IFAC World Congress, 1999.
[9] Willke, T. Five technologies expected to change the pipe line industry,
Pipe Line & Gas Industry, January 1998.
[10] Mongkorn Klingajay, Nicola Ivan Giannoccaro, The monitoring of
autonomous threaded fastening based on curve fitting and lsm
estimatione, Proceedings of The International Association of Science
and Technology for Development IASTED) on Robotics and
Applications (RA2005), Cambridge, USA, November 2005.
[11] Mongkorn Klingajay, Sirisorn Mitranon, The optimization of an
autonomous real-time process using curve fitting signature signal,
Proceedings of the IEEE International Conference on Robotics,
Automation and Mechatronics (RAM 2008), Chengdu, China, June
2008.
[1] Campbell, G. Rogers, K. Gilbert, J. Pirat - a system for quantitative
sewer assessment, Interna¬tional No Dig-95, Dresden, Germany, 1995.
[2] Fitzgibbon, A. Pilu, M. Fisher R. Direct Least Square Fitting of Ellipses.
Pattern analysis and machine intelligence, Vol. 21 Issue 5, pp 476-480,
1999.
[3] Gonzalez R.C. Digital Image Processing. Addison-Wesley, MA, 1987.
[4] Halir, R. Flusser J.: Numerically stable direct least squares fitting of
ellipses. The Sixth International Conference in Central Europe on
Computer Graphics and Visualization, Plze├▓, pp. 125-132, 1998.
[5] Moselhi, O. Shehab-Eldeen, T. Automated detection of surface defects
in water and sewer pipes. Automation in Construction 8, pp 581-588,
1999.
[6] Pace, NG. Ultrasonic surveying of fully charged sewage pipes,
Electronics and Communications Engineering Journal, pp 87-92, 1994.
[7] Romero, A. Applications and benefits of using camera technology to
internally inspect polyethylene main service piping", American Gas
Association Operations Conference, Clevehand, Ohio, USA, May 1999.
[8] Roth, H, Schilling, K. Navigation and Control for Pipe Inspection and
Repair Robots: Proc of IFAC World Congress, 1999.
[9] Willke, T. Five technologies expected to change the pipe line industry,
Pipe Line & Gas Industry, January 1998.
[10] Mongkorn Klingajay, Nicola Ivan Giannoccaro, The monitoring of
autonomous threaded fastening based on curve fitting and lsm
estimatione, Proceedings of The International Association of Science
and Technology for Development IASTED) on Robotics and
Applications (RA2005), Cambridge, USA, November 2005.
[11] Mongkorn Klingajay, Sirisorn Mitranon, The optimization of an
autonomous real-time process using curve fitting signature signal,
Proceedings of the IEEE International Conference on Robotics,
Automation and Mechatronics (RAM 2008), Chengdu, China, June
2008.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:55518", author = "Mongkorn Klingajay and Tawatchai Jitson", title = "Real-time Laser Monitoring based on Pipe Detective Operation", abstract = "The pipe inspection operation is the difficult detective
performance. Almost applications are mainly relies on a manual
recognition of defective areas that have carried out detection by an
engineer. Therefore, an automation process task becomes a necessary
in order to avoid the cost incurred in such a manual process. An
automated monitoring method to obtain a complete picture of the
sewer condition is proposed in this work. The focus of the research is
the automated identification and classification of discontinuities in
the internal surface of the pipe. The methodology consists of several
processing stages including image segmentation into the potential
defect regions and geometrical characteristic features. Automatic
recognition and classification of pipe defects are carried out by means
of using an artificial neural network technique (ANN) based on
Radial Basic Function (RBF). Experiments in a realistic environment
have been conducted and results are presented.", keywords = "Artificial neural network, Radial basic function,Curve fitting, CCTV, Image segmentation, Data acquisition.", volume = "2", number = "6", pages = "782-6", }