Kangba Region of Sichuan based on swin transformer visual model research on the identification of facades of ethnic buildings.

The protection and restoration of existing buildings requires accurate acquisition of the characteristics of the building facade. The complex, diverse, and irregular distribution characteristics of the building facade components of ethnic minorities have led to a huge workload of field research, surveying, mapping, and calculation, and it is more difficult to extract its facade characteristics accurately. This study proposes a visual model based on the Swin Transformer and applies it to the graphic recognition of ethnic building elevations. The model combines the advantages of the migration learning method and deep neural network technology and is further enriched by layer normalization to improve the stability and extraction ability of model training. In the field survey of ethnic minority buildings in Kangba, Sichuan, 1100 images of local buildings were collected, including 8 different types of ethnic minority buildings. The experimental results show that compared with other mainstream deep neural network models, the Swin Transformer visual model shows excellent predictive performance to prove the effectiveness of the proposed method. This study also uses the t-sne dimension reduction method to verify the feature extraction ability of the Swin Transformer, which contributes to the protection and restoration of ethnic minority buildings, active exploration of energy conservation, digital archiving, and more. Provide theoretical and practical reference in the fields of architectural style and cultural research.