<title>Intelligent segmentation of industrial radiographic images using neural networks</title>

Abstract

An application of machine vision, incorporating neural networks, which aims to fully automate real-time radiographic inspection in welding process is described. The current methodology adopted comprises two distinct stages - the segmentation of the weld from the background content of the radiographic image, and the segmentation of suspect defect areas inside the weld region itself. In the first stage, a back propagation neural network has been employed to adaptively and accurately segment the weld region from a given image. The training of the network is achieved with a single image showing a typical weld in the run which is to be inspected, coupled with a very simple schematic weld 'template'. The second processing stage utilizes a further backpropagation network which is trained on a test set of image data previously segmented by a conventional adaptive threshold method. It is shown that the two techniques can be combined to fully segment radiographic weld images.