用开源数据+YOLOv11自动定位公路裂缝，指导高效养护
Automated Road Crack Localization for Spatially Guided Highway Maintenance
- 结合航拍图与开放地图数据微调YOLOv11检测裂缝
- 裂缝分类F1-score达0.84（正类），0.97（负类）
- 新提出瑞士裂缝密度指数，适合城市道路养护决策
公路网络对经济至关重要。气候变化导致的温差加剧了路面应力，使养护成本上升。亟需精准高效的养护策略。本研究探索开源数据在公路基础设施维护中的潜力。所提框架融合航空影像与OpenStreetMap（OSM），微调YOLOv11实现高速公路裂缝定位。为验证框架实际应用价值，计算了瑞士相对裂缝密度（RHCD）指数以支持全国养护规划。裂缝分类模型对正类（裂缝）的F1-score为0.84，对负类（无裂缝）为0.97。瑞士RHCD指数与长期地表温度振幅（LT-LST-A）相关性弱（皮尔逊r = -0.05），与交通量（TV）相关性较弱（r = 0.17），表明该新指数在养护指引中具有独立价值。高RHCD值集中出现在城市中心和交叉口附近，与预测结果具上下文一致性。研究凸显开源数据共享对公共部门创新的推动作用，助力更高效解决方案落地。
原文摘要 · Abstract (English)
Highway networks are crucial for economic prosperity. Climate change-induced temperature fluctuations are exacerbating stress on road pavements, resulting in elevated maintenance costs. This underscores the need for targeted and efficient maintenance strategies. This study investigates the potential of open-source data to guide highway infrastructure maintenance. The proposed framework integrates airborne imagery and OpenStreetMap (OSM) to fine-tune YOLOv11 for highway crack localization. To demonstrate the framework's real-world applicability, a Swiss Relative Highway Crack Density (RHCD) index was calculated to inform nationwide highway maintenance. The crack classification model achieved an F1-score of $0.84$ for the positive class (crack) and $0.97$ for the negative class (no crack). The Swiss RHCD index exhibited weak correlations with Long-term Land Surface Temperature Amplitudes (LT-LST-A) (Pearson's $r\ = -0.05$) and Traffic Volume (TV) (Pearson's $r\ = 0.17$), underlining the added value of this novel index for guiding maintenance over other data. Significantly high RHCD values were observed near urban centers and intersections, providing contextual validation for the predictions. These findings highlight the value of open-source data sharing to drive innovation, ultimately enabling more efficient solutions in the public sector.
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