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Proceedings Paper

Automated inspection of tubular material based on magnetic particle inspection
Author(s): Adhiguna Mahendra; Christophe Stolz; Fabrice Meriaudeau; Sebastien Petit; Alexandre Noel; Fabien Degoutin
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Paper Abstract

Automatic industrial surface inspection methodology based on Magnetic Particle Inspection is developed from image acquisition to defect classification. First the acquisition system is optimized, then tubular material images are acquired, reconstructed then stored. The characteristics of the crack-like defects with respect to its geometric model and curvature are used as a priori knowledge for mathematical morphology and linear filtering. After the segmentation and binarization of the image, vast amount of defect candidates exist. Finally classification is performed with decision tree learning algorithm due to its robustness and speed. The parameters for mathematical morphology, linear filtering and classification are analyzed and optimized with Design Of Experiments based on Taguchi approach. The most significant parameters obtained may be analyzed and tuned further. Experiments are performed on tubular materials and evaluated by its accuracy and robustness by comparing ground truth and processed images. The result is promising with 97 % True Positive and only 0.01 % False Positive rate on the testing set.

Paper Details

Date Published: 2 February 2012
PDF: 15 pages
Proc. SPIE 8300, Image Processing: Machine Vision Applications V, 830007 (2 February 2012); doi: 10.1117/12.906710
Show Author Affiliations
Adhiguna Mahendra, Lab. d'Electronique, d'Informatique et d'Image, CNRS, Univ. de Bourgogne (France)
Vallourec S.A. (France)
Christophe Stolz, Lab. d'Electronique, d'Informatique et d'Image, CNRS, Univ. de Bourgogne (France)
Fabrice Meriaudeau, Lab. d'Electronique, d'Informatique et d'Image, CNRS, Univ. de Bourgogne (France)
Sebastien Petit, Vallourec S.A. (France)
Alexandre Noel, Vallourec S.A. (France)
Fabien Degoutin, Vallourec S.A. (France)

Published in SPIE Proceedings Vol. 8300:
Image Processing: Machine Vision Applications V
Philip R. Bingham; Edmund Y. Lam, Editor(s)

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