Interpretable Deep Learning for Marble Tiles Sorting

Abstract

One of the main problems in the final stage of the production line of ornamental stone tiles is the process of quality control and product classification. Successful classification of natural stone tiles based on their aesthetical value can raise profitability. Machine learning is a technology with the capability to fulfil this task with a higher speed than conventional human expert based methods. This paper examines the performance of 15 convolutional neural networks in sorting dolomitic stone tiles as far as models’ accuracy and interpretability are concerned. For the first time, these two performance indices of deep learning models are studied massively for the industrial application of machine vision based marbles sorting. The experiments revealed that the examined convolutional neural networks are able to predict the quality of the marble tiles in an industrial environment accurately in an interpretable way. Furthermore, the DenseNet201 model showed the best accuracy of 83.24%, a performance, which is supported by the consideration of the appropriate quality patterns from the marble tiles’ surface.