Automatic Stones Classification through a CNN-Based Approach.

This paper presents an automatic recognition system for classifying stones belonging to different Calabrian quarries (Southern Italy). The tool for stone recognition has been developed in the SILPI project (acronym of ""), financed by POR Calabria FESR-FSE 2014-2020. Our study is based on the (CNNs) that is used in literature for many different tasks such as speech recognition, neural language processing, bioinformatics, image classification and much more. In particular, we propose a two-stage hybrid approach based on the use of a model of (DL), in our case the CNN, in the first stage and a model of (ML) in the second one. In this work, we discuss a possible solution to stones classification which uses a CNN for the feature extraction phase and the or (MLR), (SVM), (kNN), (RF) and (GNB) ML techniques in order to perform the classification phase basing our study on the approach called (TL). We show the image acquisition process in order to collect adequate information for creating an opportune database of the stone typologies present in the Calabrian quarries, also performing the identification of quarries in the considered region. Finally, we show a comparison of different DL and ML combinations in our Two-Stage Hybrid Model solution.