AUTOMATIC DAMAGE DETECTION OF STONE CULTURAL PROPERTY BASED ON DEEP LEARNING ALGORITHM

22 citations as recorded by crossref.

1. CWADE-Net: a deep learning framework for vegetation invasion and brick spalling defect detection on Nanjing Ming City Wall X. Yuan et al. https://doi.org/10.1038/s40494-026-02681-7
2. An Analysis of Research Trends for Using Artificial Intelligence in Cultural Heritage F. Gîrbacia https://doi.org/10.3390/electronics13183738
3. New AI challenges for cultural heritage protection: A general overview F. Colace et al. https://doi.org/10.1016/j.culher.2025.07.019
4. Automatic recognition of craquelure and paint loss on polychrome paintings of the Palace Museum using improved U-Net Q. Yuan et al. https://doi.org/10.1186/s40494-023-00895-7
5. Deterioration Extraction for Ancient Murals Via a Decision Tree with a Deterioration Index K. Qiao et al. https://doi.org/10.1007/s10812-025-01977-z
6. Transforming Architectural Digitisation: Advancements in AI-Driven 3D Reality-Based Modelling K. Zhang & F. Fassi https://doi.org/10.3390/heritage8020081
7. Artificial Intelligence for Detecting Surface Alteration Phenomena in Stone-Built Heritage: The Case of the ‘Unfinished Church’ of Venosa M. Buldo et al. https://doi.org/10.36253/tema-16919
8. Artificial intelligence-assisted visual inspection for cultural heritage: State-of-the-art review M. Mishra & P. Lourenço https://doi.org/10.1016/j.culher.2024.01.005
9. PrecisionGAN: enhanced image-to-image translation for preserving structural integrity in skeletonized images M. Ahmed et al. https://doi.org/10.1007/s10032-024-00505-7
10. An automatic annotation method for colored 3D triangular meshes oriented to cultural relic deterioration segmentation C. Hu et al. https://doi.org/10.1038/s40494-026-02421-x
11. Digital Toolkit to Assist the Interpretation of Traditional Masonry Construction A. Forster et al. https://doi.org/10.1080/15583058.2023.2182729
12. Machine learning assisted real-time acoustic monitoring of laser cleaning in Heritage conservation G. Barmparis et al. https://doi.org/10.1038/s40494-025-02146-3
13. Complex Texture Contour Feature Extraction of Cracks in Timber Structures of Ancient Architecture Based on YOLO Algorithm J. Ma et al. https://doi.org/10.1155/2022/7879302
14. Historic Built Environment Assessment and Management by Deep Learning Techniques: A Scoping Review V. Giannuzzi & F. Fatiguso https://doi.org/10.3390/app14167116
15. Automated Surface Crack Detection in Historical Constructions with Various Materials Using Deep Learning-Based YOLO Network N. Karimi et al. https://doi.org/10.1080/15583058.2024.2376177
16. Intelligent assessment system of material deterioration in masonry tower based on improved image segmentation model J. Zou & Y. Deng https://doi.org/10.1186/s40494-024-01366-3
17. Using mask R-CNN to rapidly detect the gold foil shedding of stone cultural heritage in images M. Hou et al. https://doi.org/10.1186/s40494-024-01158-9
18. Evaluating Activation Functions in GAN Models for Virtual Inpainting: A Path to Architectural Heritage Restoration A. Maitin et al. https://doi.org/10.3390/app14166854
19. A deep learning-based pipeline for the conservation assessment of bindings in archives and libraries V. Lee-Gouet et al. https://doi.org/10.1007/s11042-025-20615-6
20. Artificial intelligence-based visual inspection system for structural health monitoring of cultural heritage M. Mishra et al. https://doi.org/10.1007/s13349-022-00643-8
21. A comprehensive review of deep learning methods in damage classification, detection, and segmentation of cultural heritage sites A. ElBehairy et al. https://doi.org/10.1016/j.culher.2026.01.015
22. Applications of Artificial Intelligence and Machine Learning in the Preservation and Analysis of Heritage Structures: A Comprehensive Review H. Sharma https://doi.org/10.1007/s11831-025-10393-7