Deep Learning-Based Intelligent Monument Recognition and Augmented Reality Visualization for Interactive Cultural Heritage Exploration
Authors-S.Venkateswara Rao, Udutha Bhanu Sri
Keyword-Deep Learning, Monument Recognition, Cultural Heritage, VGG16, Convolutional Neural Network, Transfer Learning, Image Classification, Augmented Reality, Android Application, Computer Vision
The preservation and promotion of historical monuments have become increasingly important as digital technologies continue to reshape cultural education and tourism. Conventional monument recognition techniques generally rely on manual searches or static information systems, making it difficult for visitors to obtain accurate and engaging historical information during site visits. To overcome these limitations, this research introduces an intelligent monument recognition frame-work that combines deep learning with augmented reality to provide an interactive cultural herit-age experience. The proposed system employs the VGG16 convolutional neural network as the primary feature extraction model for recognizing monuments captured through mobile cameras. A lightweight convolutional neural network is also implemented to compare classification per-formance under identical experimental conditions. Before classification, images undergo prepro-cessing operations including resizing, normalization, grayscale conversion, and feature en-hancement. The recognized monument is linked with historical descriptions, geographical loca-tion, and three-dimensional augmented reality visualization, allowing users to explore monu-ments through an immersive digital interface. The complete framework is deployed as an An-droid application, enabling real-time monument identification and visualization using mobile devices. Experimental evaluation demonstrates that the transfer learning capability of VGG16 produces superior classification performance compared with the conventional CNN architecture. The VGG16 model achieves an overall testing accuracy of 98.01%, whereas the standard CNN records 95.34% accuracy using the same dataset. The integration of augmented reality further improves user engagement by presenting historical information within the surrounding environ-ment instead of traditional text-based interfaces. The proposed framework offers a practical solution for digital heritage preservation, smart tourism, and educational applications while main-taining high recognition accuracy under different viewing conditions.
Doi-[https://doi.org/10.5281/zenodo.21735846]