VISAPP 2008 - 3rd International Conference on Computer Vision Theory and Applications, Proceedings
- Volumen: 2
- Fecha: 17 December 2008
- Páginas: 260-263
- ISBN: 9789898111210
- Source Type: Conference Proceeding
- Document Type: Conference Paper
Radiographic inspection is a well-established testing method to detect weld defects. However, interpretation of radiographic films is a difficult task. The reliability of such interpretation and the expense of training suitable experts have allowed that the efforts being made towards automation in this field. 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 three regularisation process with different architectures. 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.