Artificial intelligence assists the identification and application of rural heritage wall surface damage in Zhejiang

Rural cultural heritage is a valuable testament to history and culture, with traditional dwelling walls serving as key research objects. However, in Zhejiang Province, humid conditions and weathering have led to damage such as cracks, holes, stains, and yellowing, posing protection challenges. This study employs the YOLOv8 deep learning model, leveraging multi-layer feature extraction and feature fusion to enhance automated damage detection. Field experiments in Hangzhou and Lishui involved data collection, annotation, model training, and testing. Results indicate high accuracy in yellowing (0.8) and stain detection (F1-score 0.71), with improved performance under high-resolution conditions. Additionally, a Raspberry Pi 5-based mobile detection system enables real-time monitoring, providing a cost-effective and efficient solution for cultural heritage protection.