Surface quality control of ceramic tiles using neural networks approach

The image processing described in this paper is used for visual quality control in ceramic tile production. The tile surface quality depends on the surface defects. The described image processing is based on the neural network approach. The described diagnostic algorithm is presented to detect surface failures on white and coloured ceramic tiles. The tiles are scanned and the digital images are preprocessed and classified using neural networks. Preprocessing of the image data is used to keep the number of inputs of the neural networks performing the classification relatively small. It is important to reduce the amount of input data with problem specific pre-processing. The auto-associative neural network is used for feature generation and selection while the probabilistic neural network is used for classification. Simulation was performed in Matlab using the Neural Network Toolbox. The algorithm is evaluated experimentally using the real tile images. The analysis of the detection capabilities and sensitivity expressed in nondetected failures and false proclaimed defect is done also. The results obtained were satisfactory considering the fact that the images were scanned under the normal conditions. The developing and testing of this method is used for early design of the computer aided visual control.