用Mamba结构提升裂缝分割精度与效率，兼顾复杂形态适应性。
Topology-aware Mamba for Crack Segmentation in Structures
- 基于VMambaV2编码器+新设计解码器，结合蛇形扫描模块增强特征提取。
- 在CrackSeg9k和SewerCrack数据集上达到顶尖性能，且在视网膜血管分割任务中表现良好。
- 适合需要高效高精度裂缝检测的基础设施健康监测场景。
CrackMamba是一种基于Mamba的模型，旨在实现基础设施结构健康监测中裂缝分割的高效与高精度。传统卷积神经网络（CNN）因感受野有限而受限，尽管视觉变换器（ViT）提升了分割精度，但计算开销大。CrackMamba采用预训练ImageNet-1k权重的VMambaV2作为编码器，并设计新型解码器以提升性能。为应对裂缝发展的随机性和复杂性，提出蛇形扫描（Snake Scan）模块，重构裂纹特征序列以增强特征提取；同时引入三分支蛇形卷积状态空间块（SCVSS），更精准定位裂缝。实验表明，CrackMamba在CrackSeg9k和SewerCrack数据集上达到当前最优（SOTA）性能，并在视网膜血管分割数据集CHASE_DB1上表现出竞争力，验证了其泛化能力。代码已公开：https://github.com/shengyu27/CrackMamba。
原文摘要 · Abstract (English)
CrackMamba, a Mamba-based model, is designed for efficient and accurate crack segmentation for monitoring the structural health of infrastructure. Traditional Convolutional Neural Network (CNN) models struggle with limited receptive fields, and while Vision Transformers (ViT) improve segmentation accuracy, they are computationally intensive. CrackMamba addresses these challenges by utilizing the VMambaV2 with pre-trained ImageNet-1k weights as the encoder and a newly designed decoder for better performance. To handle the random and complex nature of crack development, a Snake Scan module is proposed to reshape crack feature sequences, enhancing feature extraction. Additionally, the three-branch Snake Conv VSS (SCVSS) block is proposed to target cracks more effectively. Experiments show that CrackMamba achieves state-of-the-art (SOTA) performance on the CrackSeg9k and SewerCrack datasets, and demonstrates competitive performance on the retinal vessel segmentation dataset CHASE\underline{~}DB1, highlighting its generalization capability. The code is publicly available at: {https://github.com/shengyu27/CrackMamba.}
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