Análise da classificação de símbolos de arte rupestre usando a inteligência artificial a partir da plataforma Orange Canvas

Abstract

The classification of rock art symbols, based on the similarity between their various shapes, is essential for the analysis of these ancient symbols. However, the manual classification process is time-consuming, laborious, and prone to errors. Thus, this study investigates the use of the Orange Canvas platform and its feasibility to automate this task, using Machine Learning models. Thus, 3,137 images of rock art symbols were analyzed, divided into three categories: anthropomorphs, circles, and hands. Before training and classification, the images underwent preprocessing involving resizing, cropping, and conversion to compatible formats, in addition to the data augmentation process. For feature extraction, convolutional neural network models, such as Inception V3, SqueezeNet, VGG-16, and VGG-19, were tested on the Orange Canvas platform, and the classification was performed using the algorithms: Neural Network, SVM, and Logistic Regression. The results indicated that the Inception V3 model achieved the best performance, reaching 97% accuracy, F1 Score, Precision and Recall, with Neural Network. With these metrics, the approach demonstrated to be efficient in the classification of rock art symbols, contributing to the automated analysis of these symbols. It is concluded that the use of the aforementioned approach in the classification of rock art symbols is promising.