mlsanityData to 7 Oct 2026

SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in Structures

arXiv 2503.01113 ↗First posted 3 Mar 2025cs.CVcited by 16

On the map: State-space sequence models (S4, Mamba) → Point clouds, detection and remote sensing

Abstract (arXiv)

Pixel-level segmentation of structural cracks across various scenarios remains a considerable challenge. Current methods encounter challenges in effectively modeling crack morphology and texture, facing challenges in balancing segmentation quality with low computational resource usage. To overcome these limitations, we propose a lightweight Structure-Aware Vision Mamba Network (SCSegamba), capable of generating high-quality pixel-level segmentation maps by leveraging both the morphological information and texture cues of crack pixels with minimal computational cost. Specifically, we developed a Structure-Aware Visual State Space module (SAVSS), which incorporates a lightweight Gated Bottleneck Convolution (GBC) and a Structure-Aware Scanning Strategy (SASS). The key insight of GBC lies in its effectiveness in modeling the morphological information of cracks, while the SASS enhances the perception of crack topology and texture by strengthening the continuity of semantic information between crack pixels. Experiments on crack benchmark datasets demonstrate that our method outperforms other state-of-the-art (SOTA) methods, achieving the highest performance with only 2.8M parameters. On the multi-scenario dataset, our method reached 0.8390 in F1 score and 0.8479 in mIoU. The code is available at https://github.com/Karl1109/SCSegamba.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 32 of its 61 references that are in the map. First posted on arXiv 3 Mar 2025.

Builds on

32 of 61 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024CM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation17 May
  2. 2024FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba15 Apr

Other references · 30

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  6. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  7. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
  8. 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 1,979
  9. 2019Generating Long Sequences with Sparse Transformerscited by 1,523
  10. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,051
  11. 2024VMamba: Visual State Space Modelcited by 955
  12. 2016Language Modeling with Gated Convolutional Networkscited by 688
18 more
  1. 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 614
  2. 2018Free-Form Image Inpainting with Gated Convolutioncited by 594
  3. 2022On the Parameterization and Initialization of Diagonal State Space Modelscited by 418
  4. 2024MambaIR: A Simple Baseline for Image Restoration with State-Space Modelcited by 349
  5. 2024SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentationcited by 266
  6. 2022It's Raw! Audio Generation with State-Space Modelscited by 171
  7. 2024PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognitioncited by 141
  8. 2019Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detectioncited by 80
  9. 2021Is Attention Better Than Matrix Decomposition?cited by 75
  10. 2024ViT-CoMer: Vision Transformer with Convolutional Multi-scale Feature Interaction for Dense Predictionscited by 65
  11. 2023Learning to Upsample by Learning to Samplecited by 57
  12. 2021RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depthcited by 33
  13. 2023A Convolutional-Transformer Network for Crack Segmentation with Boundary Awarenesscited by 12
  14. 2023Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulationcited by 10
  15. 2023A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentationcited by 8
  16. 2024Rediscovering BCE Loss for Uniform Classificationcited by 7
  17. 2024Staircase Cascaded Fusion of Lightweight Local Pattern Recognition and Long-Range Dependencies for Structural Crack Segmentationcited by 6
  18. 2024LORS: Low-rank Residual Structure for Parameter-Efficient Network Stackingcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 14.

16 papers.

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By area:

  1. 2025LIDAR: Lightweight Adaptive Cue-Aware Fusion Vision Mamba for Multimodal Segmentation of Structural Crackscited by 3
  2. 2026MixerCSeg: An Efficient Mixer Architecture for Crack Segmentation via Decoupled Mamba Attentioncited by 3
  3. 2026FMS2: Unified Flow Matching for Segmentation and Synthesis of Thin Structurescited by 2
  4. 2025Deep Learning for Crack Detection: A Review of Learning Paradigms, Generalizability, and Datasetscited by 1
  5. 2026Rethinking Efficient Crack Segmentation with Task-Aligned Structural-Directional Modelingcited by 1
  6. 2026Fluxamba: Topology-Aware Anisotropic State Space Models for Geological Lineament Segmentation in Multi-Source Remote Sensingcited by 0
  7. 2026Synergistic Perception and Generative Recomposition: A Multi-Agent Orchestration for Expert-Level Building Inspectioncited by 0
  8. 2026Bridging the Geometry Mismatch: Frequency-Aware Anisotropic Serialization for Thin-Structure SSMscited by 0
  9. 2026Diff-KD: Diffusion-based Knowledge Distillation for Collaborative Perception under Corruptionscited by 0
  10. 2026PaveBench: A Versatile Benchmark for Pavement Distress Perception and Interactive Vision-Language Analysiscited by 0
  11. 2026Laplacian Frequency Interaction Network for Rural Thematic Road Extractioncited by 0
  12. 2026SCRWKV: Ultra-Compact Structure-Calibrated Vision-RWKV for Topological Crack Segmentationcited by 0
4 more
  1. 2026Rapid-Deployment Crack Measurement Based on SAM3 Semantic-Edge Response Decodingcited by 0
  2. 2026SPDCN: Strip-based Deformable Convolutional Network for Steel Surface Defect Segmentationcited by 0
  3. 2026Compass: Degradation-Simulated Reciprocal Learning with Lightweight Needle RWKV for Multimodal Crack Segmentation under Missing Modalitiescited by 0
  4. 2026CoRe-SAM3: Conditional Semantic--Visual Reconciliation for SAM3 Crack Segmentationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Mamba meets crack segmentation
  2. 2024Topology-aware Mamba for Crack Segmentation in Structures
  3. 2024Vision Mamba-based autonomous crack segmentation on concrete, asphalt, and masonry surfaces
  4. 2024MSCrackMamba: Leveraging Vision Mamba for Crack Detection in Fused Multispectral Imagery
  5. 2026Cracks in the Foundation: A Civil Infrastructure Dataset to Challenge Vision Foundation Models
  6. 2026CrackSegFlow: Controllable Flow Matching Synthesis for Generalizable Crack Segmentation with a 50K Image-Mask Benchmark
  7. 2026An End-to-End Automated Pipeline for Controllable Crack Data Synthesis
  8. 2024Visual Mamba: A Survey and New Outlooks