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<p>Accurate detection of blast furnace tuyere leaks is critical for operational safety and energy efficiency in the steel industry. However, significant challenges arise from the scarcity of real-world datasets and the subtle, ambiguous nature of leak-related features. Here, we propose a cross-domain detection framework guided by a sparse set of target samples to effectively bridge the sim-to-real gap. The framework incorporates a multi-dynamic attention network designed as a feature enhancement module within the detector’s backbone. By employing a progressive serial fusion strategy, this module amplifies the discriminative representation of faint, multi-scale leak patterns. Furthermore, we introduce a region-refined domain adaptation strategy that utilizes a spatially selective adversarial focal mechanism. Unlike conventional global alignment approaches, this method leverages specific positive and negative samples through RoI-based alignment to achieve precise, region-focused domain adaptation. Extensive experiments conducted on both synthetic and real-world industrial datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches in terms of detection accuracy and cross-domain robustness.</p>

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Robust cross-domain blast furnace tuyere leak detection via few-shot guided simulation-to-real adaptation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月12日

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  • Chengjie Huang,
  • Wei Wang,
  • Lei Sun,
  • Zhuonan Li,
  • Yu Song,
  • Junchao Zhu

摘要

Accurate detection of blast furnace tuyere leaks is critical for operational safety and energy efficiency in the steel industry. However, significant challenges arise from the scarcity of real-world datasets and the subtle, ambiguous nature of leak-related features. Here, we propose a cross-domain detection framework guided by a sparse set of target samples to effectively bridge the sim-to-real gap. The framework incorporates a multi-dynamic attention network designed as a feature enhancement module within the detector’s backbone. By employing a progressive serial fusion strategy, this module amplifies the discriminative representation of faint, multi-scale leak patterns. Furthermore, we introduce a region-refined domain adaptation strategy that utilizes a spatially selective adversarial focal mechanism. Unlike conventional global alignment approaches, this method leverages specific positive and negative samples through RoI-based alignment to achieve precise, region-focused domain adaptation. Extensive experiments conducted on both synthetic and real-world industrial datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches in terms of detection accuracy and cross-domain robustness.