SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in Structures

• URL: http://arxiv.org/abs/2503.01113v3
• Date: Sun, 23 Mar 2025 13:59:45 GMT
• Title: SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in Structures
• Authors: Hui Liu, Chen Jia, Fan Shi, Xu Cheng, Shengyong Chen,
• Abstract summary: We propose a lightweight Structure-Aware Vision Mamba Network (SCSegamba) to generate high-quality pixel-level segmentation maps.<n>Specifically, we developed a Structure-Aware Visual State Space module (SAVSS), which incorporates a lightweight Gated Bottleneck Convolution (GBC) and a Structure-Aware Scanning Strategy (SASS)<n> Experiments on crack benchmark datasets demonstrate that our method outperforms other state-of-the-art (SOTA) methods, achieving the highest performance with only 2.8M parameters.
• Score: 29.224360412743454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pixel-level segmentation of structural cracks across various scenarios remains a considerable challenge. Current methods encounter challenges in effectively modeling crack morphology and texture, facing challenges in balancing segmentation quality with low computational resource usage. To overcome these limitations, we propose a lightweight Structure-Aware Vision Mamba Network (SCSegamba), capable of generating high-quality pixel-level segmentation maps by leveraging both the morphological information and texture cues of crack pixels with minimal computational cost. Specifically, we developed a Structure-Aware Visual State Space module (SAVSS), which incorporates a lightweight Gated Bottleneck Convolution (GBC) and a Structure-Aware Scanning Strategy (SASS). The key insight of GBC lies in its effectiveness in modeling the morphological information of cracks, while the SASS enhances the perception of crack topology and texture by strengthening the continuity of semantic information between crack pixels. Experiments on crack benchmark datasets demonstrate that our method outperforms other state-of-the-art (SOTA) methods, achieving the highest performance with only 2.8M parameters. On the multi-scenario dataset, our method reached 0.8390 in F1 score and 0.8479 in mIoU. The code is available at https://github.com/Karl1109/SCSegamba.

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