The Fine Feature Extraction and Attention Re-Embedding Model Based on the Swin Transformer for Pavement Damage Classification

The accurate detection and classification of pavement damage are critical for ensuring timely maintenance and extending the service life of road infrastructure. In this study, we propose a novel pavement damage recognition model based on the Swin Transformer architecture, specifically designed to address the challenges inherent in pavement imagery, such as low damage visibility, varying illumination conditions, and highly similar surface textures. Unlike the original Swin Transformer, the proposed model incorporates two key components: a fine feature extraction module and a multi-head self-attention re-embedding module. These additions enhance the model’s ability to capture subtle and complex damage patterns. Experimental evaluations demonstrate that the proposed model achieves a 2.07% improvement in classification accuracy and a 0.97% increase in F1 score compared to the baseline while maintaining comparable computational complexity. Overall, the model significantly outperforms the baseline Swin Transformer in pavement damage detection and classification, highlighting its practical applicability.