A Recognition Method for Architectural Decorative Motifs in Guanzhong Traditional Vernacular Dwellings for the Digital Documentation of Architectural Heritage

Guanzhong traditional vernacular dwellings are an important component of traditional architectural heritage. Their architectural decorative motifs are often attached to components such as screen walls, gable end piers, doors and windows, door pillow stones, column bases, tile ends, and roof ridges, making them important visual objects for image based documentation, typological organization, and conservation management of architectural heritage. Since the relevant images are mainly obtained through field photography, field images often contain multiple sources of interference, including weathering, spalling, occlusion, contamination, illumination variation, wall joints, brick joints, shadows, component edges, and damaged textures. These factors lead to blurred motif boundaries, strong background interference, and a high risk of misrecognition. Existing motif recognition methods mainly focus on clear samples and pay insufficient attention to candidate region generation, invalid texture filtering, and the exclusion of low confidence results in complex field scenarios. To address these problems, this study proposes a recognition method for architectural decorative motifs in complex scenarios. Based on field collected images of Guanzhong traditional vernacular dwellings, the method takes architectural decorative motifs and non-motif candidate regions as training objects. SAM is first used to generate potential motif candidate regions. Spatial-prior filtering based on candidate size, aspect ratio, and SAM stability is then introduced to reduce invalid region proposals, while semantic aggregation is used to retain large candidate regions supported by high-confidence local motif evidence. Swin Transformer is further adopted as the classification backbone to extract local texture features and overall structural relationships from candidate regions. A maximum confidence based rejection mechanism is also introduced to enhance the model’s ability to exclude low confidence predictions and regions without motifs. Component-level test-set evaluation and expert-verified complete-field-image evaluation show that the proposed method can reduce the interference of complex backgrounds and invalid textures, and improve the recognition stability of architectural decorative regions in field images. This study provides technical support for image organization, digital documentation, typological indexing, and conservation management of architectural decorative motifs in traditional vernacular dwellings.