Automatic Inspection System of Welding Radiographic Images Based on ANN Under a Regularisation Process

In this paper, we describe an ANN with a modified performance function which is used in an automatic inspection 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 using a γ parameter in its function, for different neurons in the input and hidden layer in order to obtain a better performance on the classification stage. The automatic system of recognition and classification proposed consists in detecting the four main types of weld defects met in practice plus the non-defect type. The results was compared with the aim to know the parameters that allow the best classification. The correlation coefficients, confusion matrix and the accuracy or the proportion of the total number of predictions that were correct was determined obtaining a value of 80% for the ANN using a modified performance function with a parameter γ=0.6.

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