Deep learning and digital twin integration for structural damage detection in ancient pagodas

In recent years, with the rise of digital twin technology in the field of artificial intelligence and the continuous advancement of hardware imaging equipment, significant progress has been made in the detection of structural damage in buildings and sculptures. Structural damage to cultural heritage buildings poses a major threat to their integrity, making accurate detection of such damage crucial for cultural heritage preservation. However, existing deep learning-based object detection technologies face limitations in achieving full coverage of architectural sculptures and enabling multi-angle, free observation, while also exhibiting substantial detection errors. To address these challenges, this paper proposes a detection method that integrates digital modeling with an improved YOLO algorithm. By scanning architectural scenes to generate digital twin models, this method enables full-angle and multi-seasonal scene transformations. Specifically, the Nanjing Sheli pagoda is selected as the research subject, where drone-based panoramic scanning is employed to create a digitalized full-scene model. The improved YOLO algorithm is then used to evaluate detection performance under varying weather and lighting conditions. Finally, evaluation metrics are utilized to automatically analyze detection accuracy and the extent of damage. Compared to traditional on-site manual measurement methods, the proposed YOLO-based automatic detection technology in digitalized scenarios significantly reduces labor costs while improving detection accuracy and efficiency. This approach provides a highly effective and reliable technical solution for assessing the extent of damage in historical buildings.