This work addresses the problem of automatic tunnel surface defect inspection using unmanned aerial vehicles (UAVs). The research aims at proposing a robust and efficient monitoring method for image data acquisition and processing in complex and dark tunnel environments. A method, called Proximity Move-Pause-Photo for Surface Defect Inspection (PMPP-SDI), is proposed by combining reactive flying control strategies with a grid scanning pattern to capture high-quality image data from multiple views and angles. The image data is then used to generate a 3D point cloud model of the tunnel surface for structural condition assessment. The method is tested in a field experiment in a railway tunnel in Ireland, and the results show that it can achieve stable navigation, high-resolution reconstruction, and accurate defect detection. The paper discusses the advantages and limitations of the method, and suggests improving the control/navigation intelligence, data quality, and defect analysis as the future research directions.

中文翻译：

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基于反应式无人机的自动隧道表面缺陷检测与现场测试

这项工作解决了使用无人机（UAV）进行自动隧道表面缺陷检查的问题。该研究旨在提出一种稳健、高效的监测方法，用于复杂和黑暗隧道环境中的图像数据采集和处理。提出了一种称为表面缺陷检测的接近移动-暂停-照片（PMPP-SDI）的方法，该方法将反应式飞行控制策略与网格扫描模式相结合，从多个视角和角度捕获高质量的图像数据。然后使用图像数据生成隧道表面的 3D 点云模型，以进行结构状况评估。该方法在爱尔兰铁路隧道进行现场实验测试，结果表明该方法能够实现稳定导航、高分辨率重建和准确的缺陷检测。文章讨论了该方法的优点和局限性，并建议提高控制/导航智能、数据质量和缺陷分析作为未来的研究方向。