A Multi-Scale Attention-Enhanced YOLOv26 Framework for Steel Structure Corrosion Detection and Segmentation

Steel structures in complex service environments are highly susceptible to corrosion, making accurate detection challenging. This study proposes an improved YOLOv26-based method for corrosion damage segmentation. A diverse dataset is constructed by combining field-collected and public data with varying lighting conditions and multi-scale features. Enhancements to the YOLOv26-seg architecture include integrating Efficient Channel Attention (ECA) in the backbone to strengthen low-contrast feature representation, designing a multi-branch attention mechanism (ECA + CBAM) in the detection head to improve small- and medium-scale target recognition, and introducing Selective Kernel Attention (SKA) in the segmentation branch to refine boundary details. The resulting YOLOv26-ECS model achieves an mAP50 of 0.920 and mAP50–95 of 0.851 on the self-constructed dataset, outperforming the baseline by 5.0% and 6.0%, respectively, while maintaining 28.34 FPS. Experiments on public datasets further demonstrate strong generalization. A GUI system is also developed for visualization and practical deployment. Overall, the proposed method delivers accurate and efficient corrosion detection and segmentation, showing strong potential for engineering applications.