Enhancing bridge damage detection with Mamba-Enhanced HRNet for semantic segmentation

Abstract

With the acceleration of urbanization, bridges, as crucial infrastructure, their structural health and stability are paramount to public safety. This paper proposes Mamba-Enhanced HRNet for bridge damage detection. Mamba-Enhanced HRNet integrates the advantages of HRNet’s multi-resolution parallel design and VMamba’s visual state space model. By replacing the residual convolutional blocks in HRNet with a combination of VSS blocks and convolution, this model enhances the network’s capability to capture global contextual information while maintaining computational efficiency. This work builds an extensive dataset with multiple damage kinds and uses Mean Intersection over Union (Mean IoU) as the assessment metric to assess the performance of Mamba-Enhanced HRNet. Experimental results demonstrate that Mamba-Enhanced HRNet achieves significant performance improvements in bridge damage semantic segmentation tasks, with Mean IoU scores of 0.963, outperforming several other semantic segmentation models.