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Proceedings Paper

Detection, location, and quantification of structural damage by neural-net-processed moire profilometry
Author(s): Barry G. Grossman; Frank S. Gonzalez; Joel H. Blatt; Jeffery A. Hooker
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Paper Abstract

The development of efficient high speed techniques to recognize, locate, and quantify damage is vitally important for successful automated inspection systems such as ones used for the inspection of undersea pipelines. Two critical problems must be solved to achieve these goals: the reduction of nonuseful information present in the video image and automatic recognition and quantification of extent and location of damage. Artificial neural network processed moire profilometry appears to be a promising technique to accomplish this. Real time video moire techniques have been developed which clearly distinguish damaged and undamaged areas on structures, thus reducing the amount of extraneous information input into an inspection system. Artificial neural networks have demonstrated advantages for image processing, since they can learn the desired response to a given input and are inherently fast when implemented in hardware due to their parallel computing architecture. Video moire images of pipes with dents of different depths were used to train a neural network, with the desired output being the location and severity of the damage. The system was then successfully tested with a second series of moire images. The techniques employed and the results obtained are discussed.

Paper Details

Date Published: 1 March 1992
PDF: 12 pages
Proc. SPIE 1614, Optics, Illumination, and Image Sensing for Machine Vision VI, (1 March 1992); doi: 10.1117/12.57980
Show Author Affiliations
Barry G. Grossman, Florida Institute of Technology (United States)
Frank S. Gonzalez, Florida Institute of Technology (United States)
Joel H. Blatt, Florida Institute of Technology (United States)
Jeffery A. Hooker, Florida Institute of Technology (United States)

Published in SPIE Proceedings Vol. 1614:
Optics, Illumination, and Image Sensing for Machine Vision VI
Donald J. Svetkoff, Editor(s)

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