Maintaining airport runways is crucial for safety and efficiency, yet traditional monitoring relies on manual inspections, prone to time consumption and inaccuracy. This study pioneers the utilization of low‐cost dashcam imagery for the detection and geolocation of airport runway pavement distresses, employing novel deep‐learning frameworks. A significant contribution of our work is the creation of the first public dataset specifically designed for this purpose, addressing a critical gap in the field. This dataset, enriched with diverse distress types under various environmental conditions, enables the development of an automated, cost‐effective method that substantially enhances airport maintenance operations. Leveraging low‐cost dashcam technology in this unique scenario, our approach demonstrates remarkable potential in improving the efficiency and safety of airport runway inspections, offering a scalable solution for infrastructure management. Our findings underscore the benefits of integrating advanced imaging and artificial intelligence technologies, paving the way for advancements in airport maintenance practices.

中文翻译：

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使用行车记录仪图像进行人工智能机场跑道路面破损检测

维护机场跑道对于安全和效率至关重要，但传统监控依赖于人工检查，容易耗时且不准确。这项研究开创了利用低成本行车记录仪图像来检测和地理定位机场跑道路面问题的先河，采用新颖的深度学习框架。我们工作的一个重大贡献是创建了第一个专门为此目的设计的公共数据集，解决了该领域的关键空白。该数据集丰富了各种环境条件下的各种遇险类型，可以开发出一种自动化、经济高效的方法，从而大大增强机场维护运营。在这种独特的场景中利用低成本行车记录仪技术，我们的方法在提高机场跑道检查的效率和安全性方面展示了巨大的潜力，为基础设施管理提供了可扩展的解决方案。我们的研究结果强调了集成先进成像和人工智能技术的好处，为机场维护实践的进步铺平了道路。