Deep Learning Applications for Identifying and Detecting Heritage Sites

This research work introduces a novel application of Artificial Intelligence (AI) in monument identification, utilizing the state-of-the-art object detection model YOLOv8. With the aim of revolutionizing the study, preservation, and understanding of historical sites, the proposed method employs image and video data for detection. A comprehensive dataset of historical temples was collected and annotated for training the system. Convolutional Neural Networks (CNNs) and the YOLOv8 algorithm, advanced deep learning techniques, were employed to locate and identify individual monuments within intricate historical contexts. Furthermore, sophisticated metric evaluation methods were integrated into the system to provide real-time insights into model behavior, detection reliability, potential biases, and ongoing monitoring, eliminating the need for third-party platforms. This feature enables rapid adjustments and enhancements, resulting in a dependable and effective monument detection system. Additionally, the model incorporates a Text-To-Speech (TTS) system to vocalize the names, historical contexts, and other relevant information of the identified heritage monuments.