PREDICTION OF STONE TYPES USING CONVOLUTIONAL NEURAL NETWORKS TECHNIQUE

Abstract

This paper investigates using Convolutional Neural Networks (CNNs), specifically the MobileNetV2 architecture, for predicting stone types. The research focused on classifying five stone categories – granite, marble, limestone, sandstone, and slate – using a dataset of 2,500 images. The CNN model was trained over 100 epochs, achieving a high training accuracy of 89.6%, demonstrating its capability to learn and identify distinct patterns within stone images. However, the model faced challenges with overfitting, as evidenced by the testing accuracy stabilizing around 60%, indicating difficulties in generalizing to unseen data. Evaluation of key performance metrics, including precision, recall, and F1 score, showed strong performance in identifying stone types like limestone and sandstone but highlighted areas needing improvement, such as distinguishing granite and marble. The study underscores the potential of CNNs for stone-type classification and proposes future enhancements through techniques like data augmentation, ensemble learning, and transfer learning to improve generalization and predictive accuracy. This research provides valuable insights into applying CNNs in material classification within geological contexts.