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<p>To address the challenges of low accuracy and limited real-time efficiency in detecting subsurface defects within concrete structures, this study proposes an enhanced YOLOv5 model integrated with an Efficient Channel Attention (ECA) mechanism for automated ground-penetrating radar (GPR) defect detection. A Deep Convolutional Generative Adversarial Network (DCGAN)-based augmentation strategy is introduced to mitigate class imbalance, synthesizing realistic minority-class defect samples while preserving wave scattering characteristics.​​ A specialized dataset encompassing diverse defect types was constructed to reflect real-world concrete inspection scenarios. The proposed YOLOv5 + ECA model was rigorously evaluated against other attention-enhanced variants and the baseline YOLOv5. Experimental results demonstrate that ECA’s channel-specific feature recalibration significantly improves detection accuracy, achieving the highest mean average precision, while maintaining real-time inference speeds suitable for unmanned aerial vehicle (UAV)-mounted deployment. This work advances the precision and efficiency of infrastructure health monitoring, offering a robust solution for subsurface defect diagnosis in concrete structures such as tunnel linings and bridge decks.</p>

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Real-time defect detection in concrete structures using attention-based deep learning and GPR imaging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月10日

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  • Jia-Yu Zhang,
  • Liang Huang,
  • Yu-Jian Guan

摘要

To address the challenges of low accuracy and limited real-time efficiency in detecting subsurface defects within concrete structures, this study proposes an enhanced YOLOv5 model integrated with an Efficient Channel Attention (ECA) mechanism for automated ground-penetrating radar (GPR) defect detection. A Deep Convolutional Generative Adversarial Network (DCGAN)-based augmentation strategy is introduced to mitigate class imbalance, synthesizing realistic minority-class defect samples while preserving wave scattering characteristics.​​ A specialized dataset encompassing diverse defect types was constructed to reflect real-world concrete inspection scenarios. The proposed YOLOv5 + ECA model was rigorously evaluated against other attention-enhanced variants and the baseline YOLOv5. Experimental results demonstrate that ECA’s channel-specific feature recalibration significantly improves detection accuracy, achieving the highest mean average precision, while maintaining real-time inference speeds suitable for unmanned aerial vehicle (UAV)-mounted deployment. This work advances the precision and efficiency of infrastructure health monitoring, offering a robust solution for subsurface defect diagnosis in concrete structures such as tunnel linings and bridge decks.