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Analysis of microtomographic images in automatic defect localization and detection

Authors
  • Marzec, Mariusz1
  • Duda, Piotr1
  • Wróbel, Zygmunt1
  • 1 University of Silesia, ul. Bedzinska 39, Sosnowiec, 41-200, Poland , Sosnowiec (Poland)
Type
Published Article
Journal
Machine Vision and Applications
Publisher
Springer-Verlag
Publication Date
May 27, 2020
Volume
31
Issue
5
Identifiers
DOI: 10.1007/s00138-020-01084-3
Source
Springer Nature
Keywords
License
Green

Abstract

The paper presents a fast method of fully automatic localization and classification of defects in aluminium castings based on computed microtomography images. In the light of current research and based on available publications, where such analysis is made on the basis of images obtained from standard radiography (x-ray), this is a new approach which uses microtomographic images (μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu $$\end{document}-CT). In addition, the above-mentioned solutions most often analyze a pre-separated portion of an image, which requires the initial operator interference. The authors’ own pre-processing methods, which allow to separate the element area and potential defect areas from μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu $$\end{document}-CT images, and methods of extraction of selected features describing these areas have been proposed in the solution discussed here. A neural network trained using the Levenberg–Marquardt method with error backpropagation has been used as a classifier. The optimal network structure 20–4–1 and a set of 20 features describing the analysed areas have been determined as a result of performed tests. The applied solutions have provided 89% correct detection for any defect size and 96.73% for large defects, which is comparable to the results obtained from methods using x-ray images. This has confirmed that it is possible to use μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu $$\end{document}-CT images in automatic defect localization in 3D. Thanks to this method, quantitative analysis of aluminium castings can be carried out without user interaction and fully automated.

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