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

  • Rafael Vilar
  • Juan Zapata
  • Ramón Ruiz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5602)


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.


Hide Layer Performance Function Radiographic Image Weld Region Welding Defect 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rafael Vilar
    • 1
  • Juan Zapata
    • 2
  • Ramón Ruiz
    • 2
  1. 1.Departamento de Estructuras y ConstrucciónSpain
  2. 2.Departamento de Electrónica, Tecnología de Computadores y ProyectosUniversidad Politécnica de CartagenaCartagenaSpain

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