AI RESEARCH
Rethinking Efficient Crack Segmentation with Task-Aligned Structural-Directional Modeling
arXiv CS.CV
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ArXi:2605.31048v1 Announce Type: new Recent crack segmentation methods often follow generic semantic segmentation designs, using stronger backbones, hybrid CNN-Transformer-Mamba encoders, and auxiliary enhancement branches. Although effective, this raises whether stronger generic feature mixing is the most suitable direction for crack segmentation. We instead formulate crack segmentation as sparse structural recovery.