Conference Paper

Weld defects recognition and classification based on ann

Conference Proceeding cp
Proceedings of the 5th IASTED International Conference on Signal Processing, Pattern Recognition, and Applications, SPPRA 2008
  • Fecha: 01 December 2008
  • Páginas: 181-186
  • ISBN: 9780889867178
  • Source Type: Conference Proceeding
  • Document Type: Conference Paper
In this paper, we describe an automatic detection system to recognise 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. For the input layer, the principal component analysis technique was used in order to reduce the number of feature variables; and, for the hidden layer, a different number of neurons was used in the aim to give better performance for defect classification in both cases. The proposed classification consists in detecting the four main types of weld defects met in practice plus the non-defect type.

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