Optimizing Semantic Segmentation of Corrosion Defects with Coordinated Attention and Feature Integration

The corrosion segmentation technique powered by deep learning enables early detection and timely intervention, effectively preventing further progression and ensuring the safety and extended service life of steel structures. To address the challenges of complex boundaries and fine-grained features in corrosion regions, this study proposes an enhanced high-resolution network version 2 (HRNetV2) model, termed HRNetV2-corrosion, for high-precision corrosion segmentation and analysis. First, an interlayer feature fusion module was designed to improve the integration of high- and low-level features, thereby enhancing fine-grained feature representation. Second, a global feature fusion module is introduced to strengthen the model’s ability to capture global context. To improve computational efficiency, depthwise separable convolution is employed in the output module, significantly reducing complexity and accelerating inference speed. Furthermore, the integration of the channel attention mixer with spatial refinement (CAMixerSR) and coordinate attention (CoordAtt) mechanisms enhances detail extraction and improves the recognition of blurred boundaries. Experimental results demonstrated that HRNetV2-corrosion not only outperforms traditional models across multiple evaluation metrics but also achieves superior efficiency. In addition, a graded evaluation method based on corrosion area is proposed, which classifies corrosion into distinct levels through quantitative analysis of segmentation results, providing a reliable basis for repair and maintenance decisions. Overall, HRNetV2-corrosion offers an effective solution for high-precision corrosion detection and demonstrates substantial practical value.