Extraction and Classification of Road Markings Using Mobile Laser Scanning Point Clouds
Extraction and Classification of Road Markings Using Mobile Laser Scanning Point Clouds
复制标题
使用移动激光扫描点云提取和分类道路标记
DOI：
10.1109/jstars.2016.2606507
复制
发表时间：
2017-03-01
影响因子：
5.5
通讯作者：
Li, Jonathan
中科院分区：
文献类型：
--
作者：
Cheng, Ming;Zhang, Haocheng;Li, Jonathan
针对移动的激光扫描（MLS）点云数据中道路标线信息的半自动提取问题，提出了一种稳健的道路标线信息提取方法。所提出的工作流程包括三个部分：1）预处理，2）提取，和3）分类。在预处理中，三维（3-D）MLS点云被转换成辐射校正和增强的二维（2-D）强度图像的路面。然后，自动提取路面标记的强度使用一组算法，包括大津的阈值，邻居计数过滤，和区域生长。最后，提取的路面标线的几何参数进行分类，通过使用一个手动定义的决策树。使用在中国福建厦门获得的MLS数据集进行了研究。实验结果表明，所提出的工作流程和方法可以达到92%的完整性，95%的正确性，和94%的F-评分。
This study aims at building a robust method for semiautomated information extraction of pavement markings detected from mobile laser scanning (MLS) point clouds. The proposed workflow consists of three components: 1) preprocessing, 2) extraction, and 3) classification. In preprocessing, the three-dimensional (3-D) MLS point clouds are converted into radiometrically corrected and enhanced two-dimensional (2-D) intensity imagery of the road surface. Then, the pavement markings are automatically extracted with the intensity using a set of algorithms, including Otsu's thresholding, neighbor-counting filtering, and region growing. Finally, the extracted pavement markings are classified with the geometric parameters by using a manually defined decision tree. A study was conducted by using the MLS dataset acquired in Xiamen, Fujian, China. The results demonstrated that the proposed workflow and method can achieve 92% in completeness, 95% in correctness, and 94% in F-score.