  Cracks pose safety risks to infrastructure and cannot be overlooked. The
prevailing structures in existing crack segmentation networks predominantly
consist of CNNs or Transformers. However, CNNs exhibit a deficiency in global
modeling capability, hindering the representation to entire crack features.
Transformers can capture long-range dependencies but suffer from high and
quadratic complexity. Recently, Mamba has garnered extensive attention due to
its linear spatial and computational complexity and its powerful global
perception. This study explores the representation capabilities of Mamba to
crack features. Specifically, this paper uncovers the connection between Mamba
and the attention mechanism, providing a profound insight, an attention
perspective, into interpreting Mamba and devising a novel Mamba module
following the principles of attention blocks, namely CrackMamba. We compare
CrackMamba with the most prominent visual Mamba modules, Vim and Vmamba, on two
datasets comprising asphalt pavement and concrete pavement cracks, and steel
cracks, respectively. The quantitative results show that CrackMamba stands out
as the sole Mamba block consistently enhancing the baseline model's performance
across all evaluation measures, while reducing its parameters and computational
costs. Moreover, this paper substantiates that Mamba can achieve global
receptive fields through both theoretical analysis and visual interpretability.
The discoveries of this study offer a dual contribution. First, as a
plug-and-play and simple yet effective Mamba module, CrackMamba exhibits
immense potential for integration into various crack segmentation models.
Second, the proposed innovative Mamba design concept, integrating Mamba with
the attention mechanism, holds significant reference value for all Mamba-based
computer vision models, not limited to crack segmentation networks, as
investigated in this study.
