Automatic classification of ornamental stones using Machine Learning techniques - a study applied to limestone.

Abstract

The industry of extraction and transformation of rock minerals has an enormous importance in the Portuguese trade balance. The export volume increases every year, and to maintain these results it is necessary to invest in the modernization and optimization of production processes, as well as, in the classification of raw materials. This study aims to implement a classification model of ornamental rocks through the analysis and classification of images, using machine learning algorithms. The recognition of the type of stone, through the capture of images and subsequent algorithmic analysis, will allow to define quality control scales in future processes, taking into account the different types of stone. In addition, it will also allow to develop models capable of helping in reducing the amount of raw material wasted. This work presents the steps taken to create a classification model, using a dataset of 2260 images distributed over four classes, three of which are very similar to color level and one with a different tone. In this study, the results of the application of three automatic classification algorithms are analyzed. In addition, a discussion of how types of images can improve results and the execution times of algorithms are presented.