SPDCN: Strip-based Deformable Convolutional Network for Steel Surface Defect Segmentation
Steel surface defect segmentation is critical for industrial quality inspection, yet existing methods struggle with elongated, anisotropic defects such as cracks and scratches due to the isotropic receptive fields of standard convolutions and rigid sampling grids that cannot adapt to irregular defect boundaries. To address these limitations, we propose Strip-based Predictor for Deformable Convolutional Networks (SPDCN) with two key innovations. The \textbf{Fuzzy-enhanced Multi-scale Context Module (FMCM)} employs group-wise multi-branch convolutions with an intuitionistic fuzzy channel attenti
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- FuzzyOverlapping authors or contributors · 62%affaan-m/ECC →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%pytorch/pytorch →
“Shared author/contributor keys: zou”
- FuzzyOverlapping authors or contributors · 62%BerriAI/litellm →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: wan”
- LinkedLinked via arxiv author · 85%Zhongming Liu →
“SPDCN: Strip-based Deformable Convolutional Network for Steel Surface Defect Segmentation”
- LinkedLinked via arxiv author · 85%Bingbing Jiang →
“SPDCN: Strip-based Deformable Convolutional Network for Steel Surface Defect Segmentation”
- LinkedLinked via arxiv author · 85%Guangxin Wan →
“SPDCN: Strip-based Deformable Convolutional Network for Steel Surface Defect Segmentation”