Automated texture classification of marble shades with real-time PLC neural network implementation

The subjective evaluation of marbles based on their visual appearance could be replaced by an automated texture classification system, intending to achieve high classification accuracy and production effectiveness. The existing marble classification methods from a computational point of view are either too complex or very expensive. Nowadays some inspection systems in marble industry that automates the quality-control tasks and shade classification are too expensive and are compatible only with specific technological equipment. In this paper a new approach for classification of marble tiles with similar shades is proposed. It is based on simple image preprocessing, on training a MLP neural network (MLP NN) with marble histograms and implementation of the algorithm in a Programmable Logic Controller (PLC) for real-time execution. A method for training the MLP NN aiming optimization of MLP parameters and topology is proposed. The designed automated system uses only standard PLC modules and communication interfaces. The experimental test results when recognizing marble textures with added motion blur are represented and discussed. The performance of the modeling technique is assessed with different training and test sets. The classification accuracy results are compared to other results obtained by similar approaches.