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

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.