Detection and location of pipe damage by artificial-neural-net-processed moire error maps

Barry G. Grossman; Frank S. Gonzalez; Joel H. Blatt; Scott Christian Cahall

doi:10.1117/12.145558

28 May 1993 Detection and location of pipe damage by artificial-neural-net-processed moire error maps

Barry G. Grossman, Frank S. Gonzalez, Joel H. Blatt, Scott Christian Cahall

Proceedings Volume 1821, Industrial Applications of Optical Inspection, Metrology, and Sensing; (1993) https://doi.org/10.1117/12.145558
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States

Abstract

A novel automated inspection technique to recognize, locate, and quantify damage is developed. This technique is based on two already existing technologies: video moire metrology and artificial neural networks. Contour maps generated by video moire techniques provide an accurate description of surface structure that can then be automated by means of neutral networks. Artificial neural networks offer an attractive solution to the automated interpretation problem because they can generalize from the learned samples and provide an intelligent response for similar patterns having missing or noisy data. Two dimensional video moire images of pipes with dents of different depths, at several rotations, were used to train a multilayer feedforward neural network by the backpropagation algorithm. The backpropagation network is trained to recognize and classify the video moire images according to the dent's depth. Once trained, the network outputs give an indication of the probability that a dent has been found, a depth estimate, and the axial location of the center of the dent. This inspection technique has been demonstrated to be a powerful tool for the automatic location and quantification of structural damage, as illustrated using dented pipes.

Citation Download Citation

Barry G. Grossman, Frank S. Gonzalez, Joel H. Blatt, and Scott Christian Cahall "Detection and location of pipe damage by artificial-neural-net-processed moire error maps", Proc. SPIE 1821, Industrial Applications of Optical Inspection, Metrology, and Sensing, (28 May 1993); https://doi.org/10.1117/12.145558

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