Work

CWADE-Net: a deep learning framework for vegetation invasion and brick spalling defect detection on Nanjing Ming City Wall

Year: 2026

Type: article

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.

Source: npj Heritage Science

DOI: 10.1038/s40494-026-02681-7