Abstract
Focusing on the cultural heritage of the Nanjing Ming Dynasty city wall, this study presents CWADE-Net, a deep learning-based city wall anomaly detection framework specially designed to detect surface defects arising from herbaceous/woody and vine-type vegetation invasion, as well as brick spalling. Its novelty lies in the capability to address challenging conditions such as uneven illumination, complex backgrounds, and large defect-scale variations. CWADE-Net jointly integrates illumination enhancement, edge information encoding, and spatial-frequency feature extraction in its backbone to improve feature representation. Its neck employs bidirectional feature fusion to enhance multi-scale semantic interaction. Moreover, we adopt a lightweight detection head that enables real-time model deployment. Experiments on images acquired using a Nikon D300, iPhone 15 Pro Max, and DJI Matrice 4E demonstrate mAP50 scores of 82.4%, 87.9%, and 54.8% for three defect types, outperforming mainstream methods by 5-12%, thus effectively supporting intelligent monitoring, conservation, and World Cultural Heritage nomination efforts.
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 42571513, and Grant 42271450, in part by the Major Program of the National Natural Science Foundation of China under Grant 42293272, and in part by the Project of Anhui Provincial Department of Education Scientific Research under Grant 2025AHGXZK31210.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Yuan, X., Wang, N., Wang, Y. et al. CWADE-Net: a deep learning framework for vegetation invasion and brick spalling defect detection on Nanjing Ming City Wall. npj Herit. Sci. (2026). https://doi.org/10.1038/s40494-026-02681-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s40494-026-02681-7
