Intelligent segmentation of industrial radiographic images using neural networks

An application of machine vision, incorporating neural networks, which aims to fully automate real-time radiographic inspection in welding processes 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 utilises 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.