A multi-scale transformer-enhanced YOLO framework for unified road damage detection and boundary-aware segmentation

Road surface deterioration poses significant challenges for transportation safety, infrastructure maintenance, and intelligent transportation management. Automated road damage analysis based on computer vision has therefore become an important research topic in recent years. This study proposes a novel deep learning framework for multi-task road damage analysis that integrates detection, classification, and segmentation within a unified architecture. The proposed model incorporates multi-scale feature extraction, transformer-based contextual refinement, ROI-based classification enhancement, and a boundary-aware segmentation module to improve the identification of diverse pavement defects. Multi-scale alignment enables the network to capture road damage patterns at different spatial resolutions, while contextual attention mechanisms strengthen feature relationships across complex road scenes. The boundary-aware segmentation branch further improves structural understanding of pavement defects by enabling precise contour delineation. Extensive experiments demonstrate that the proposed architecture achieves superior performance compared with conventional detection models, improving both detection accuracy and segmentation quality while maintaining practical inference speed. Ablation studies further confirm the effectiveness of each architectural component and highlight the contribution of contextual feature refinement and boundary-aware learning to overall model performance. The results indicate that the proposed framework provides a reliable and efficient solution for automated road condition monitoring and intelligent infrastructure inspection. This approach can support real-time road maintenance systems and contribute to safer and more efficient transportation networks.