针对钢板缺陷分割中长条形裂纹难以识别的问题，提出新型可变形卷积网络。
SPDCN: Strip-based Deformable Convolutional Network for Steel Surface Defect Segmentation
- 用分条式可变形卷积，让采样网格随缺陷方向自适应调整
- 在公开数据集上达到89.60% mIoU，参数量仅354万
- 特别适合检测裂缝、划痕等细长型工业缺陷
钢板表面缺陷分割对工业质量检测至关重要，但现有方法在处理裂纹、划痕等细长、各向异性缺陷时表现不佳，原因在于标准卷积的各向同性感受野和固定采样网格无法适应不规则缺陷边界。为此，本文提出基于条带的可变形卷积网络（SPDCN），包含两项创新：1）模糊增强多尺度上下文模块（FMCM），通过分组多分支卷积与直觉模糊通道注意力机制，自适应捕捉不同尺寸缺陷的多尺度上下文信息；2）自适应方向感知可变形卷积（ADADC），将传统偏移预测器替换为解耦的水平与垂直条带卷积，使可变形采样网格能沿缺陷主方向非均匀对齐。在多个公开钢板缺陷数据集上的实验表明，SPDCN持续优于当前最优方法，在NEU-Seg数据集上达到89.60% mIoU，参数量仅为354万。源代码已公开于https://github.com/DWlzm。
原文摘要 · Abstract (English)
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 attention mechanism to adaptively capture multi-scale contextual information across varying defect sizes. The \textbf{Adaptive Direction-Aware Deformable Convolution (ADADC)} replaces the conventional offset predictor with decoupled horizontal and vertical strip convolutions, enabling the deformable sampling grid to anisotropically align with the principal orientation of elongated defects. Extensive experiments on public steel surface defect benchmarks demonstrate that SPDCN consistently outperforms state-of-the-art methods, achieving 89.60\% mIoU on NEU-Seg with only 3.54M parameters. The source code is publicly available at https://github.com/DWlzm .
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