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<p>Surface defect detection on steel components is crucial for quality control in polysilicon production. However, this task remains challenging due to tiny defect sizes, irregular geometries, complex backgrounds, and low contrast. To address these issues, we propose MSEOD-DDFusionNet (Multi-Scale and Effective Object-Detection Diffusion Fusion Network), a novel multi-scale diffusion-enhanced attention network. The network integrates four specialized modules: MTECAAttention (Multi-Scale Texture Enhancement Channel-Aware Attention) for lossless multi-scale feature fusion, ODConv (Omni-Dimensional Dynamic Convolution) for dynamic adaptation to irregular geometries, LMDP (Local Multi-Scale Discriminative Perception) for selective noise suppression and micro-defect amplification, and DDFusion (Diffusion-Driven Feature Fusion) for scene-aware noise modeling. Pruning further reduces computational complexity while improving accuracy. Extensive experiments on the specialized DDTE dataset and public benchmarks demonstrate state-of-the-art performance. Our model achieves 82.6% <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\hbox {mAP}_{50}\)</EquationSource> </InlineEquation> and 61.6% <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\hbox {mAP}_{50-95}\)</EquationSource> </InlineEquation> on DDTE, while maintaining a high inference speed of 193.5 FPS with only 8.46M parameters. It also shows excellent generalization across NEU-DET, GC10-DET, and cross-domain tasks, providing an efficient and accurate solution for industrial defect inspection.</p>

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Multiscale diffusion-enhanced attention network for steel surface defect detection in Polysilicon Production

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月16日

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  • Yiwei Duan,
  • Lizhen He,
  • Zhisheng Wang,
  • Jinhai Sa,
  • Jiawen Yang,
  • Xiaolong Chen,
  • Bingdong Shi,
  • Yangyang Zhang,
  • Jiawen Sun

摘要

Surface defect detection on steel components is crucial for quality control in polysilicon production. However, this task remains challenging due to tiny defect sizes, irregular geometries, complex backgrounds, and low contrast. To address these issues, we propose MSEOD-DDFusionNet (Multi-Scale and Effective Object-Detection Diffusion Fusion Network), a novel multi-scale diffusion-enhanced attention network. The network integrates four specialized modules: MTECAAttention (Multi-Scale Texture Enhancement Channel-Aware Attention) for lossless multi-scale feature fusion, ODConv (Omni-Dimensional Dynamic Convolution) for dynamic adaptation to irregular geometries, LMDP (Local Multi-Scale Discriminative Perception) for selective noise suppression and micro-defect amplification, and DDFusion (Diffusion-Driven Feature Fusion) for scene-aware noise modeling. Pruning further reduces computational complexity while improving accuracy. Extensive experiments on the specialized DDTE dataset and public benchmarks demonstrate state-of-the-art performance. Our model achieves 82.6% \(\hbox {mAP}_{50}\) and 61.6% \(\hbox {mAP}_{50-95}\) on DDTE, while maintaining a high inference speed of 193.5 FPS with only 8.46M parameters. It also shows excellent generalization across NEU-DET, GC10-DET, and cross-domain tasks, providing an efficient and accurate solution for industrial defect inspection.