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<p>Pavement defects pose serious threats to traffic safety, pavement durability, and operational efficiency. To achieve accurate and real-time identification of pavement defects, this study proposes an enhanced lightweight model, YOLO11-WLBS, which integrates four improved modules—Wavelet Transform Convolution, Lightweight Adaptive Extraction, Bidirectional Feature Pyramid Network, and Simple Attention—into the YOLO11 framework. Each module’s contribution is verified through ablation experiments. The proposed model achieves a precision of 0.947, recall of 0.895, F1-score of 0.895, mAP@0.5 of 0.944, and mAP@0.5–0.95 of 0.703, demonstrating high accuracy and efficiency. Compared with the baseline YOLO11, YOLO11-WLBS improves precision by 6.4%, recall by 15.8%, and mAP@0.5 by 12.2%, while reducing parameters by 25.5%. The model maintains excellent detection performance under extreme lighting and blurring conditions and exhibits strong generalization in cross-dataset applications. These results indicate that YOLO11-WLBS provides an efficient and robust solution for intelligent pavement defect detection and offers practical potential for real-time deployment on edge devices in pavement maintenance and infrastructure monitoring systems.</p>

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YOLO11-WLBS: an efficient model for pavement defect detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月15日

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  • Junqi Lin,
  • Pinxin Wang,
  • Yunkai Ruan,
  • Yunqiang Sun

摘要

Pavement defects pose serious threats to traffic safety, pavement durability, and operational efficiency. To achieve accurate and real-time identification of pavement defects, this study proposes an enhanced lightweight model, YOLO11-WLBS, which integrates four improved modules—Wavelet Transform Convolution, Lightweight Adaptive Extraction, Bidirectional Feature Pyramid Network, and Simple Attention—into the YOLO11 framework. Each module’s contribution is verified through ablation experiments. The proposed model achieves a precision of 0.947, recall of 0.895, F1-score of 0.895, mAP@0.5 of 0.944, and mAP@0.5–0.95 of 0.703, demonstrating high accuracy and efficiency. Compared with the baseline YOLO11, YOLO11-WLBS improves precision by 6.4%, recall by 15.8%, and mAP@0.5 by 12.2%, while reducing parameters by 25.5%. The model maintains excellent detection performance under extreme lighting and blurring conditions and exhibits strong generalization in cross-dataset applications. These results indicate that YOLO11-WLBS provides an efficient and robust solution for intelligent pavement defect detection and offers practical potential for real-time deployment on edge devices in pavement maintenance and infrastructure monitoring systems.