Mermer kalitesi sınıflandırması için derin öğrenme destekli bir model

The basic policy of marble enterprises is to establish sustainable high-quality products in a standardized manner. Identification and classification of different types of marbles is a critical task that is usually carried out by human experts. However, marble quality classification by human experts can be time-consuming, error-prone, unreliable, and subjective. Automated and computerized methods are needed to obtain more reliable, faster, and less subjective results. In this study, a deep learning model is developed in order to perform multi-classification of marble slab images with six different quality types. Some special image pre-processing operations were applied to the images for data augmentation and a special convolutional neural network (CNN) architecture was designed and implemented. It has been observed that the data augmentation approach for marble image samples has significantly improved the accuracy of the CNN model. Some outstanding results have been obtained with the proposed CNN model, which surpassed the alternative machine learning algorithms and even equalized the human experts' classification performance.