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-1 k 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_DB1, highlighting its generalization capability.

中文翻译：

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用于结构中裂缝分割的拓扑感知型 mamba


CrackMamba 是一种基于 Manba 的模型，旨在高效、准确地分割裂缝，以监控基础设施的结构健康状况。传统的卷积神经网络 （CNN） 模型难以处理有限的感受野，虽然视觉转换器 （ViT） 提高了分割精度，但它们是计算密集型的。CrackMamba 通过使用具有预训练 ImageNet-1 k 权重的 VMambaV2 作为编码器和新设计的解码器来获得更好的性能来应对这些挑战。为了处理裂纹发展的随机性和复杂性，提出了一个 Snake Scan 模块来重塑裂纹特征序列，增强特征提取。此外，提出了三分支 Snake Conv VSS （SCVSS） 模块以更有效地定位裂缝。实验表明，CrackMamba 在 CrackSeg9k 和 SewerCrack 数据集上实现了最先进的 （SOTA） 性能，并在视网膜血管分割数据集 CHASE_DB1上展示了有竞争力的性能，突出了其泛化能力。