# Mamba meets crack segmentation arXiv: https://arxiv.org/abs/2407.15714 ยท HTML: https://mlsanity.com/paper/2407.15714/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba). - Builds on 27 of its 53 references that are in the map. - First posted on arXiv 22 Jul 2024. ## Placed in - State-space sequence models (S4, Mamba) / [Point clouds, detection and remote sensing](https://mlsanity.com/ssm/point-clouds-detection-and-remote-sensing/) ## Builds on (most cited first, 27 of 27) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021) - [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016) - [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017) - [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022) - [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018) - [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018) - [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021) - [Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention](https://mlsanity.com/paper/2006.16236/) (2020) - [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021) - [Generating Long Sequences with Sparse Transformers](https://mlsanity.com/paper/1904.10509/) (2019) - [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018) - [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024) - [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024) - [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021) - [Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers](https://mlsanity.com/paper/2110.13985/) (2021) - [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019) - [Understanding the Effective Receptive Field in Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.04128/) (2017) - [CE-Net: Context Encoder Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/1903.02740/) (2019) - [Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs](https://mlsanity.com/paper/2203.06717/) (2022) - [Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation](https://mlsanity.com/paper/2402.05079/) (2024) - [Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection](https://mlsanity.com/paper/1901.06340/) (2019) - [RS3Mamba: Visual State Space Model for Remote Sensing Images Semantic Segmentation](https://mlsanity.com/paper/2404.02457/) (2024) - [Directional Connectivity-based Segmentation of Medical Images](https://mlsanity.com/paper/2304.00145/) (2023) - [Generative AIBIM: An automatic and intelligent structural design pipeline integrating BIM and generative AI](https://mlsanity.com/paper/2311.04052/) (2023) ## Built on by (most cited first, 1 of 1) - [A Novel Hybrid Approach for Retinal Vessel Segmentation with Dynamic Long-Range Dependency and Multi-Scale Retinal Edge Fusion Enhancement](https://mlsanity.com/paper/2504.13553/) (2025) ## Abstract (arXiv) 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.