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May 2, 2026SHILAP Revista de lepidopterologíaOpen Access

Automatic Road Damage Detection Based on Improved YOLO11

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Authors

SWSiwei WEICCCC Highway Consultants (China)YPYujian PENGHubei University of TechnologyHLHong-Fang LuoWuhan University of Technology

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Implication

Randomized trial demonstrates enhanced detection of road damage using an improved YOLO11 model, suggesting better maintenance solutions.

Key Points

  • The aim is to improve road damage detection by enhancing the YOLO11 model's capabilities.
  • Introduced the RoadRep-C3 module for better feature extraction.
  • Implemented an efficient multi-scale attention mechanism to capture damage features.
  • Utilized a hypergraph structure for information fusion and a slide loss function to optimize performance.
  • Achieved a 2% increase in mean average precision (mAP@0.5) compared to original YOLO11.
  • Reduced model size while maintaining high detection accuracy for small objects.

Cite This Study

WEI et al. (2026) studied this question.

synapsesocial.com/papers/69f594e171405d493afffc05https://doi.org/10.7307/ptt.v38i4.1128
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Also Consider

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  1. 1Automatic road damage recognition based on improved YOLOv11 with multi-scale feature extraction and fusion attention mechanism2025 · 11 citations
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