Classification of Welding Defects in Radiographic Images Using an ANN with Modified Performance Function

Abstract

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.KeywordsHide LayerPerformance FunctionRadiographic ImageWeld RegionWelding DefectThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.