A Classification Method of Softwood Species for Building and Interior Decoration Based on Deep Learning

Abstract

The material properties of softwood species affect the safety of building structures, and wood identification is a key factor in material certification in specific institutions for green building certification. This study investigated an efficient wood species identification algorithm, aiming to provide a reliable method for material selection in construction and decoration industries. Using microscopic cross-sectional images of 36 softwood species applied in construction and decoration as research objects, 11 classic deep learning models were employed for species classification, combined with class activation map analysis to examine the key structural features for species identification. Specifically, the model structure and advantages of Swin Transformer were highlighted, in which hierarchical feature extraction and shifted window attention mechanism enable multi-scale fusion of wood structural features, such as tracheids, within global contexts, thereby improving classification accuracy for wood cross-sectional images. Experimental results showed that the Swin Transformer model achieved the highest classification accuracy of 99.97%, with both precision and recall exceeding 99% and an F1 score of 99%. These findings validate that deep learning networks based on the Transformer framework can achieve reliable image classification performance in wood research.