Abstract: In order to detect the air bubbles in welding gap, the multi-layer Hopfield neural network is presented to segment welding X-ray image. The image segmentation is posed as an optimization problem. The energy function is constructed to meet the characteristics of welding X-ray image such as great noise and random positions of air bubbles. The principle of selecting coefficient is given through some experiments. A new algorithm for segmenting welding X-ray image is also put forward based on multi-layer Hopfield neural network. The algorithm is combined with median filtering and neural network to wipe off noise and find air bubbles effectively. As an application, the algorithm successfully segments some real industrial welding X-ray images.

Key words: Neural network, Image segmentation, Welding gap