Pattern recognition of internal structural defects in industrial radiographic testing based on neural network

Ming Ming; Zheng Li

doi:10.1117/12.441683

20 September 2001 Pattern recognition of internal structural defects in industrial radiographic testing based on neural network

Ming Ming, Zheng Li

Author Affiliations +

Proceedings Volume 4555, Neural Network and Distributed Processing; (2001) https://doi.org/10.1117/12.441683
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

Abstract

It is shown that an artificial neural network can be used to classify internal structural defects in radiographic nondestructive testing. We design a series of images presenting phantoms to simulate three different classes of defects: porosity, crack, and slag. Features of these defects are selected from domains of geometry, gray statistics, frequency spectrum, and etc. Some of them are especially suitable for pattern recognition in the case of radiographic image. A three-layered neural network trained with back-propagation rule is developed to carry out the classification. The training and testing data for the net are the features extracted from digitized radiographic images. Results are presented with satisfactory recognition rate.

Citation Download Citation

Ming Ming and Zheng Li "Pattern recognition of internal structural defects in industrial radiographic testing based on neural network", Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); https://doi.org/10.1117/12.441683

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