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<p>Defect detection in rubber–metal bushings is difficult because many faults appear as small surface-height changes rather than intensity changes. Here, we present a height-information inspection method that converts 16-bit laser profile measurements into three-channel height-color images for detector input. Paired grayscale images are used only as a baseline for modality comparison. The model replaces the standard attention block in a real-time one-stage detector with a height-aware attention module that combines feature-group channel attention, multi-scale spatial attention, and adaptive feature fusion. This design lets the network emphasize local height transitions while it retains contextual surface information. On 11,000 bushing images, height-color input alone increased recall by 45.3% over grayscale input. With the proposed attention module, the detector reached 92.9% precision, 93.5% recall, and 96.7% mean average precision at 0.5 intersection over union. Compared with representative transformer, graph-based, and multimodal detectors, the method improved mean average precision by 4.9–18.8 percentage points and reduced computation by 40–82%. These results show that joint design of image representation and network architecture improves industrial bushing inspection.</p>

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Height-aware attention for industrial defect detection: co-designing imaging and deep networks for rubber–metal bushings

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

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  • Rongfen Shen,
  • Shikun Chen,
  • Yangguang Liu

摘要

Defect detection in rubber–metal bushings is difficult because many faults appear as small surface-height changes rather than intensity changes. Here, we present a height-information inspection method that converts 16-bit laser profile measurements into three-channel height-color images for detector input. Paired grayscale images are used only as a baseline for modality comparison. The model replaces the standard attention block in a real-time one-stage detector with a height-aware attention module that combines feature-group channel attention, multi-scale spatial attention, and adaptive feature fusion. This design lets the network emphasize local height transitions while it retains contextual surface information. On 11,000 bushing images, height-color input alone increased recall by 45.3% over grayscale input. With the proposed attention module, the detector reached 92.9% precision, 93.5% recall, and 96.7% mean average precision at 0.5 intersection over union. Compared with representative transformer, graph-based, and multimodal detectors, the method improved mean average precision by 4.9–18.8 percentage points and reduced computation by 40–82%. These results show that joint design of image representation and network architecture improves industrial bushing inspection.