R. Vilar, J. Zapata, and R. Ruiz (Spain)
Weld defect, connected components, principal component analysis, artificial neuronal network.
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 re duction, contrast enhancement, thresholding and labelling, were implemented to help in the recognition of weld re gions 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 net work for weld defect classification was used under a regu larisation process. For the input layer, the principal com ponent analysis technique was used in order to reduce the number of feature variables; and, for the hidden layer, a dif ferent 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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