Surface Damage Detection and Analysis for Reduction-Fired Cyan Square Bricks in Jiangnan Gardens via YOLOv12

作者：Lina Yan, Yile Chen, Xingkang Jia, Liang Zheng · 发表于：Coatings · 年份：2025 · DOI：10.3390/coatings15091066 · 被引用次数：2 · 研究领域：3D Surveying and Cultural Heritage、Conservation Techniques and Studies、Building materials and conservation

As an outstanding UNESCO World Heritage Site, the Jiangnan gardens feature both exquisite and fragile components. Reduction-fired cyan square bricks, serving as crucial paving materials, are long-term exposed to natural and anthropogenic factors, making them prone to various types of surface damage and urgently requiring efficient, non-destructive detection methods to support scientific conservation. Traditional manual inspection methods suffer from low efficiency, strong subjectivity, and potential disturbance to the fragile heritage structures. This study focuses on developing an intelligent detection method based on advanced computer vision, employing the YOLOv12 object detection model to achieve non-contact, automated identification of typical tile surface damage types in the Jiangnan gardens (such as cracking, stains, water stains, and wear). A total of 691 images of reduction-fired cyan square bricks collected on-site were used as training samples. The main conclusions of this study are as follows: (1) By constructing a dataset containing multiple samples and multiple scenes of reduction-fired cyan square brick images in Jiangnan gardens, the YOLOv12 model was trained and optimized, enabling it to accurately identify subtle damage features under complex texture backgrounds. (2) Overall indicators: Through the comparison of the confusion matrices of the four key training nodes, model C (the 159th epoch, highest mAP50–95) has the most balanced overall performance in multi...