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<p>Steel surface defect detection remains challenging due to low-contrast textures, fine crack-like patterns, and weakly textured granular defects, particularly under uneven illumination. This paper presents SFEDet, an improved detector built upon YOLO12 with three tailored designs. First, Learnable Gray Preparation (LGP) performs learnable contrast enhancement at the input stage via a gated residual formulation. Second, DefectFreqFusion replaces conventional concatenation with spatial–frequency dual-path fusion for robust multi-scale feature integration. Third, an edge- and morphology-aware neck enhancement scheme is developed, combining C2fDE for multi-scale receptive fields, Edge-Guided Attention Module (EGAM) for Sobel edge-guided attention, and Efficient Head Preparation (EfficientHeadPre) for lightweight pre-detection refinement; together these modules constitute the Bottom-Up Steel Neck (BU-SN). Experiments on NEU-DET (640<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\times\)</EquationSource></InlineEquation>640) demonstrate that SFEDet-Large achieves 75.1% mAP@0.5, outperforming the YOLO12-L baseline by 2.5 percentage points, while the lightweight SFEDet-Nano variant attains 72.6% mAP@0.5 with only 8.65M parameters and 333 FPS inference speed. Ablation studies confirm BU-SN as the primary contributor to detection accuracy, while LGP significantly enhances robustness under varying illumination conditions, yielding an 8.5% mAP improvement in extremely dark scenarios. The proposed framework offers favorable accuracy–complexity trade-offs for industrial inspection scenarios, with SFEDet-Large prioritizing detection reliability and SFEDet-Nano enabling efficient real-time deployment.</p>

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SFEDet: edge-guided spatial-frequency fusion network for steel surface defect detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月5日

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  • Weili Chen,
  • Jianjing Wei,
  • Xin Guo,
  • Bidong Chen

摘要

Steel surface defect detection remains challenging due to low-contrast textures, fine crack-like patterns, and weakly textured granular defects, particularly under uneven illumination. This paper presents SFEDet, an improved detector built upon YOLO12 with three tailored designs. First, Learnable Gray Preparation (LGP) performs learnable contrast enhancement at the input stage via a gated residual formulation. Second, DefectFreqFusion replaces conventional concatenation with spatial–frequency dual-path fusion for robust multi-scale feature integration. Third, an edge- and morphology-aware neck enhancement scheme is developed, combining C2fDE for multi-scale receptive fields, Edge-Guided Attention Module (EGAM) for Sobel edge-guided attention, and Efficient Head Preparation (EfficientHeadPre) for lightweight pre-detection refinement; together these modules constitute the Bottom-Up Steel Neck (BU-SN). Experiments on NEU-DET (640\(\times\)640) demonstrate that SFEDet-Large achieves 75.1% mAP@0.5, outperforming the YOLO12-L baseline by 2.5 percentage points, while the lightweight SFEDet-Nano variant attains 72.6% mAP@0.5 with only 8.65M parameters and 333 FPS inference speed. Ablation studies confirm BU-SN as the primary contributor to detection accuracy, while LGP significantly enhances robustness under varying illumination conditions, yielding an 8.5% mAP improvement in extremely dark scenarios. The proposed framework offers favorable accuracy–complexity trade-offs for industrial inspection scenarios, with SFEDet-Large prioritizing detection reliability and SFEDet-Nano enabling efficient real-time deployment.