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<p>Deep learning-based image processing methods are commonly used for bridge crack detection. Aiming at the problem of missed detections and false positives caused by light, stains, and dense cracks during detection, this paper proposes a bridge crack detection algorithm based on the improved YOLOv8n model. Firstly, enhancing the model’s feature extraction capabilities by incorporating the global attention mechanism into the Backbone and Neck to gather additional crack characterization information. And optimizing the original feature fusion model through Gam-Concat to enhance the feature fusion effect. Subsequently, in the FPN-PAN structure, replacing the original upsample module with DySample promotes the full fusion of high- and low-resolution feature information, enhancing the detection capability for cracks of different scales. Finally, adding MPDIoU to the Head to optimize the bounding box function loss, enhancing the model’s ability to evaluate the overlap of dense cracks and better reflecting the spatial relationships between the cracks. In ablation and comparison experiments, the improved model achieved increases of 3.02%, 3.39%, 2.26%, and 0.81% in mAP@0.5, mAP@0.5:0.95, precision, and recall, respectively, compared to the original model. And the detection accuracy is significantly higher than other comparative models. It has practical application value in bridge inspection projects.</p>

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Improved YOLOv8n-based bridge crack detection algorithm under complex background conditions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月16日

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  • Wenyuan Xu,
  • Hao Li,
  • Guodong Li,
  • Yongcheng Ji,
  • Jianbo Xu,
  • Zhen Zang

摘要

Deep learning-based image processing methods are commonly used for bridge crack detection. Aiming at the problem of missed detections and false positives caused by light, stains, and dense cracks during detection, this paper proposes a bridge crack detection algorithm based on the improved YOLOv8n model. Firstly, enhancing the model’s feature extraction capabilities by incorporating the global attention mechanism into the Backbone and Neck to gather additional crack characterization information. And optimizing the original feature fusion model through Gam-Concat to enhance the feature fusion effect. Subsequently, in the FPN-PAN structure, replacing the original upsample module with DySample promotes the full fusion of high- and low-resolution feature information, enhancing the detection capability for cracks of different scales. Finally, adding MPDIoU to the Head to optimize the bounding box function loss, enhancing the model’s ability to evaluate the overlap of dense cracks and better reflecting the spatial relationships between the cracks. In ablation and comparison experiments, the improved model achieved increases of 3.02%, 3.39%, 2.26%, and 0.81% in mAP@0.5, mAP@0.5:0.95, precision, and recall, respectively, compared to the original model. And the detection accuracy is significantly higher than other comparative models. It has practical application value in bridge inspection projects.