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Automatic Stones Classification through a CNN-Based Approach
| Content Provider | MDPI |
|---|---|
| Author | Tropea, Mauro Fedele, Giuseppe Luca, Raffaella De Miriello, Domenico Rango, Floriano De |
| Copyright Year | 2022 |
| Description | 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 “Sistema per l’Identificazione di Lapidei Per Immagini”), financed by POR Calabria FESR-FSE 2014-2020. Our study is based on the Convolutional Neural Network (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 Deep Learning (DL), in our case the CNN, in the first stage and a model of Machine Learning (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 Softmax or Multinomial Logistic Regression (MLR), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Random Forest (RF) and Gaussian Naive Bayes (GNB) ML techniques in order to perform the classification phase basing our study on the approach called Transfer Learning (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. |
| Starting Page | 6292 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s22166292 |
| Journal | Sensors |
| Issue Number | 16 |
| Volume Number | 22 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-21 |
| Access Restriction | Open |
| Subject Keyword | Sensors Deep Learning (dl) Convolutional Neural Network (cnn) Machine Learning (ml) Softmax Support Vector Machine (svm) K-nearest Neighbors (knn) Random Forest (rf) Gaussian Naive Bayes (gnb) Two-stage Hybrid Model |
| Content Type | Text |
| Resource Type | Article |
