Abstract The condition of the road surface should be inspected to increase the service life of the road and to ensure safety and comfort. This study aims to automatically detect and measure road distress from unmanned aerial vehicle (UAV)-based images. The proposed methodology consists of three steps. First, images acquired from the UAV are used to generate the three-dimensional point cloud. Then, the road surface is extracted from the 3D point cloud. Finally, the developed algorithm is used to automatically detect and measure road distress. The accuracy assessment is conducted by comparing the analyses from point cloud data and measurements obtained from the traditional inspection method. The root mean square error values range from 2.09–6.72 cm. Finally, the outcomes of the proposed methodology are compared with those of commercial GIS software. Both produce statistically similar results for detecting road surface distress.

中文翻译：

---

一种从无人机衍生的点云中自动提取路面遇险的方法

摘要 为了提高道路的使用寿命，确保安全和舒适，应检查路面状况。本研究旨在从基于无人机 (UAV) 的图像中自动检测和测量道路遇险情况。建议的方法包括三个步骤。首先，从无人机获取的图像用于生成三维点云。然后，从 3D 点云中提取路面。最后，开发的算法用于自动检测和测量道路遇险。精度评估是通过比较点云数据的分析和传统检测方法获得的测量结果来进行的。均方根误差值范围为 2.09–6.72 cm。最后，将所提出方法的结果与商业 GIS 软件的结果进行比较。