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<p>Defect detection in printed materials is a critical yet challenging task in industrial quality control due to the small size of defects, low contrast against complex backgrounds, and the high-speed requirements of production lines. Existing detectors often struggle to capture subtle features or suppress redundant noise, leading to missed or false detections. To address these limitations, we propose HA-YOLOv8s, an enhanced YOLOv8s model built upon a hybrid-domain attention mechanism. The architecture integrates three tailored modules: (1) a Deep Multi-scale Selective Fusion (DMSF) module that combines depthwise separable residual blocks with multi-scale paths to capture both local and global semantics; (2) a Separated Enhancement Aggregation Module (SEAM) that applies spatial and channel attention to highlight critical features and suppress background interference; and (3) a Separable Pyramid Attention Detection Head (SPAD-Head) that introduces atrous convolutions, SE attention, and an additional 160 × 160 detection layer to improve scale adaptability. These modules collectively improve the model’s capability to detect small, complex defects. Experiments on a printed defect dataset and VisDrone2019 demonstrate that HA-YOLOv8s achieves mAP scores of 98.3% and 40.6%, significantly outperforming the baseline YOLOv8s. The results confirm the effectiveness and transferability of our design, making it well-suited for practical industrial inspection scenarios.</p>

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Defect detection in YOLOv8 based on a hybrid domain attention mechanism

  • 期刊论文

  • 发表日期：

    2025年9月19日

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  • Fuqiang Yang,
  • Yuanlin Zheng,
  • Kaiyang Liao,
  • Yuying Peng,
  • Haiwen Liu,
  • Rubai Luo,
  • Hanxiang Zhao,
  • Bangyong Sun

摘要

Defect detection in printed materials is a critical yet challenging task in industrial quality control due to the small size of defects, low contrast against complex backgrounds, and the high-speed requirements of production lines. Existing detectors often struggle to capture subtle features or suppress redundant noise, leading to missed or false detections. To address these limitations, we propose HA-YOLOv8s, an enhanced YOLOv8s model built upon a hybrid-domain attention mechanism. The architecture integrates three tailored modules: (1) a Deep Multi-scale Selective Fusion (DMSF) module that combines depthwise separable residual blocks with multi-scale paths to capture both local and global semantics; (2) a Separated Enhancement Aggregation Module (SEAM) that applies spatial and channel attention to highlight critical features and suppress background interference; and (3) a Separable Pyramid Attention Detection Head (SPAD-Head) that introduces atrous convolutions, SE attention, and an additional 160 × 160 detection layer to improve scale adaptability. These modules collectively improve the model’s capability to detect small, complex defects. Experiments on a printed defect dataset and VisDrone2019 demonstrate that HA-YOLOv8s achieves mAP scores of 98.3% and 40.6%, significantly outperforming the baseline YOLOv8s. The results confirm the effectiveness and transferability of our design, making it well-suited for practical industrial inspection scenarios.