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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Extraction and Classification of Road Markings Using Mobile Laser Scanning Point Clouds
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Extraction and Classification of Road Markings Using Mobile Laser Scanning Point Clouds

机译：使用移动激光扫描点云提取和分类道路标记

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摘要

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.
机译：这项研究旨在建立一种可靠的方法，用于从移动激光扫描（MLS）点云中检测到的路面标记的半自动信息提取。提议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，将三维（3-D）MLS点云转换为经过辐射线校正的增强型路面二维（2-D）强度图像。然后，使用包括Otsu的阈值，邻居计数过滤和区域增长在内的一组算法以强度自动提取路面标记。最后，通过使用手动定义的决策树，将提取的路面标记与几何参数进行分类。通过使用在中国福建厦门获得的MLS数据集进行了研究。结果表明，所提出的工作流程和方法可以实现92％的完整性，95％的正确性和94％的F评分。

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