Conference Paper

Classification of welding defects in radiographic images using an ANN with modified performance function

Book Series cp
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volumen: 5602 LNCS
  • Número: PART 2
  • Fecha: 09 November 2009
  • Páginas: 284-293
  • ISSN: 03029743 16113349
  • ISBN: 3642022669
  • Source Type: Book Series
  • DOI: 10.1007/978-3-642-02267-8_31
  • Document Type: Conference Paper
In this paper, we describe an automatic classification system of welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling, were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of geometrical features which characterise the defect shape and orientation was proposed and extracted between defect candidates. In a third stage, an artificial neural network for weld defect classification was used under a regularisation process with different architectures for the input layer and the hidden layer. Our aim is to analyse this ANN modifying the performance function for differents neurons in the input and hidden layer in order to obtain a better performance on the classification stage. © 2009 Springer Berlin Heidelberg.

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