轻量级模型精准分割结构裂缝，兼顾精度与效率
SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in Structures
- 引入结构感知状态空间模块，融合裂纹形态与纹理信息
- 仅用280万参数，多场景下F1达0.8390，mIoU达0.8479
- 适合资源受限环境下的工业裂纹检测应用
跨场景的像素级结构裂缝分割仍具挑战。现有方法在建模裂纹形态与纹理方面存在不足，难以兼顾分割质量与低计算开销。为此，我们提出轻量级结构感知视觉马尔可夫网络（SCSegamba），通过融合裂纹像素的形态信息与纹理线索，在极低计算成本下生成高质量像素级分割图。具体设计了结构感知视觉状态空间模块（SAVSS），包含轻量级门控瓶颈卷积（GBC）和结构感知扫描策略（SASS）。GBC有效捕捉裂纹形态特征，SASS通过增强裂纹像素间语义连续性，提升拓扑与纹理感知能力。在多个裂纹基准数据集上的实验表明，该方法性能优于当前主流方法，仅需280万参数，便在多场景数据集上达到0.8390的F1分数和0.8479的mIoU。代码已开源。
原文摘要 · Abstract (English)
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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