Mamba 遇上裂缝分割
Mamba meets crack segmentation
AI总结：
本研究提出 CrackMamba，一种基于注意力视角设计的 Mamba 模块，用于裂缝分割，在多个数据集上优于现有视觉 Mamba 模块，并实现全局感受野。
摘要：
裂缝对基础设施构成安全风险，不容忽视。现有裂缝分割网络中的主流结构主要由 CNN 或 Transformer 组成。然而，CNN 在全局建模能力方面存在不足，阻碍了对整体裂缝特征的表示。Transformer 能够捕获长距离依赖，但具有高且二次方的复杂度。近来，Mamba 因其线性的空间和计算复杂度以及强大的全局感知能力而受到广泛关注。本研究探讨了 Mamba 对裂缝特征的表示能力。具体而言，本文揭示了 Mamba 与注意力机制之间的联系，提供了深刻的见解，即从注意力视角来解读 Mamba，并遵循注意力模块的原理设计了一种新颖的 Mamba 模块，即 CrackMamba。我们在两个数据集上将 CrackMamba 与最著名的视觉 Mamba 模块 Vim 和 Vmamba 进行了比较，这两个数据集分别包含沥青路面和混凝土路面裂缝，以及钢裂缝。定量结果表明，CrackMamba 是唯一在所有评估指标上持续提升基线模型性能的 Mamba 模块，同时减少了其参数和计算成本。此外，本文通过理论分析和视觉可解释性证实了 Mamba 可以实现全局感受野。本研究的发现具有双重贡献。首先，作为一种即插即用且简单有效的 Mamba 模块，CrackMamba 显示出集成到各种裂缝分割模型中的巨大潜力。其次，所提出的创新性 Mamba 设计理念，即将 Mamba 与注意力机制相结合，对所有基于 Mamba 的计算机视觉模型都具有重要的参考价值，而不仅限于本研究所探讨的裂缝分割网络。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
