自适应下降距离的布料模拟路面坑槽提取算法  

Adaptive Descent Distance Cloth Simulation Algorithm for Pavement Pothole Extraction

作　　者：刘如飞[1] 许伟彬 赵倩影 苏占文 Liu Rufei;Xu Weibin;Zhao Qianying;Su Zhanwen(College of Geodesy and Geomatics,Shandong University of Science and Technology,Qingdao 266590,Shandong,China;Qingdao Geology and Minerals Rock and Soil Engineering Co.,Ltd.,Qingdao 266072,Shandong,China)

机构地区：[1]山东科技大学测绘与空间信息学院,山东青岛266590 [2]青岛地矿岩土工程有限公司,山东青岛266072

出　　处：《激光与光电子学进展》2025年第2期370-377,共8页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(42301519);山东省科技型中小企业创新能力提升工程(2022TSGC1135);“菁英计划”科研支持项目(0104060541613)。

摘　　要：针对传统布料模拟滤波(CSF)算法无法区分路面局部损坏微地形导致的坑槽病害错检、漏检问题,提出一种自适应下降距离的CSF路面坑槽提取算法。该算法首先对道路点云进行预处理去噪,获取车道路面点云。然后统计布料粒子点的深度标准差,改进CSF算法中模拟布料“外力下降”和“内力回拉”过程的位移距离,实现模拟布料的自适应距离下降,进一步构建考虑路面坑槽的精确局部基准面,生成点云深度增强信息模型。最后利用深度阈值分类和欧式聚类算法实现坑槽的精确检测并提取坑槽的几何属性特征。对道路的实测数据进行实验与分析,结果表明,实测数据中的坑槽召回率达到83.3%,精确率达到87.5%,最大面积相对误差为17.699%,最大深度相对误差为9.677%。所提算法具有一定的鲁棒性与适用性,可为大规模三维路面点云数据自动检测路面坑槽工作提供有力的支撑。Aiming at the problem that the traditonal cloth simulation filtering(CSF)algorithm cannot distinguish the local microtopography of pavement damage,which leads to the wrong detection and omission of pothole damages,an adaptive descend distance CSF algorithm for pavement pothole extraction is proposed.First,the proposed algorithm preprocesses and denoises the point cloud of the road to obtain the pavement point cloud.Second,by improving the displacement distance of the“external force drop”and“internal force pull back”processes of the simulated cloth in the CSF algorithm,the adaptive distance drop of the simulated cloth is realized,and then further constructs the accurate local datum plane of the road surface and generates the depth-enhanced information model of the point cloud.Finally,depth threshold classification and Euclidean clustering algorithm are used to achieve precise detection of potholes and extract geometric attribute features of potholes.Experiments and analysis of the measured road data show that,the recall of potholes in the measured data reaches 83.3%,and the precision reaches 87.5%,the maximum relative error of area is 17.699%,and the maximum relative error of depth is 9.677%,which has a certain degree of robustness and applicability.The proposed algorithm can provide a powerful support for the work of large-scale three-dimensional pavement point cloud data for the automatic and precise detection of potholes on pavements.

关 键 词：路面激光点云 基准面 布料模拟滤波算法 路面坑槽检测 

分 类 号：P237[天文地球—摄影测量与遥感] U418[天文地球—测绘科学与技术]