车载激光点云中交通标线自动分类与矢量化

doi:10.11947/j.AGCS.2021.20200351

测绘学报 ›› 2021, Vol. 50 ›› Issue (9): 1251-1265.doi: 10.11947/j.AGCS.2021.20200351

车载激光点云中交通标线自动分类与矢量化

方莉娜^1,2,3, 王爽^1,2,3, 赵志远^1,2,3, 付化胜⁴, 陈崇成^1,2,3

1. 福州大学地理空间信息技术国家地方联合工程研究中心, 福建福州 350002;
2. 空间数据挖掘与信息共享教育部重点实验室, 福建福州 350002;
3. 福州大学数字中国研究院, 福建福州 350002;
4. 福建省水利水电勘测设计研究院, 福建福州 350002

收稿日期:2020-07-27 修回日期:2020-11-23 发布日期:2021-10-09
作者简介:方莉娜(1983-),女,博士,助理研究员,研究方向为激光雷达数据处理与三维重建。E-mail:fangln@fzu.edu.cn
基金资助:
国家自然科学基金（42071446）；福建省对外合作项目（2020l0007）

Automatic classification and vectorization of road markings from mobile laser point clouds

FANG Lina^1,2,3, WANG Shuang^1,2,3, ZHAO Zhiyuan^1,2,3, FU Huasheng⁴, CHEN Chongcheng^1,2,3

1. National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou 350002, China;
2. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350002, China;
3. Academy of Digital China, Fuzhou University, Fuzhou 350002, China;
4. Fujian Provincial Investigation, Design&Research Institute of Water Conservancy & Hydropower, Fuzhou 350002, China

Received:2020-07-27 Revised:2020-11-23 Published:2021-10-09
Supported by:
The National Natural Science Foundation of China (No. 42071446); The Foreign Cooperation Projects of Fujian Province (No. 2020l0007)

摘要/Abstract

摘要： 交通标线是重要的交通安全设施，其位置、属性和拓扑关系精细刻画道路交通结构，是智能交通、高精地图、位置与导航等应用的基础数据。本文提出一种融合空间上下文信息的车载激光点云标线分类图注意力模型（graph attention network with spatial context information，GAT_SCNet）。该模型利用图结构建立标线及其邻接对象的出现和依存关系，基于标线几何、拓扑、空间结构关系构建注意力机制进行节点特征动态更新，通过对节点分类实现标线的精细分类。基于分类后标线，设计不同方案实现对分类后标线提取标线矢量化数据。试验采用4份不同车载激光扫描系统获取的城市与高速场景数据验证本文方法的有效性，试验结果中9类标线分类的准确率分别为100.00%、93.77%、100.00%、100.00%、100.00%、96.73%、97.96%、100.00%、98.39%，召回率分别为100.00%、96.36%、100.00%、100.00%、100.00%、97.26%、85.72%、100.00%、94.16%。结果表明，本文方法能实现道路场景中全尺寸、多类型标线对象的精确识别，并对形状相似标线（如虚线、斑马线和停止线）的区分具有较强稳健性。

关键词: 车载激光点云, 标线分类, 图结构, 注意力机制, 标线矢量化

Abstract: Road markings are important traffic safety facilities. Its location, attribute, and topological relationship finely describe road traffic structure, and it is the basic data for applications such as intelligent traffic, high-precision maps, location, and navigation. This paper proposes a graph attention network with spatial context information (GAT_SCNet) to classify the road markings from mobile LiDAR point clouds. GAT_SCNet explores the graph structure to establish the appearance and dependence information among road markings. Meanwhile, GAT_SCNet incorporates the multi-head attention mechanism into the node propagation step, which computes the hidden states of each node based on the geometric, topological, and spatial structure relationships of the neighboring nodes. Finally, road markings classification is realized by the classification of nodes. Then, some schemes are designed for road markings vectorization. Four test datasets consisting of urban and highway scenes by different mobile laser scanning systems are used to evaluate the validities of the proposed method. Four accuracy evaluation metrics precision and recall of 9 types of road markings on the selected test datasets achieve (100.00%, 93.77%, 100.00%, 100.00%, 100.00%, 96.73%, 97.96%, 100.00%, 98.39%) and (100.00%, 96.36%, 100.00%, 10.000%, 100.00%, 97.26%, 85.72%, 100.00%, 94.16%), respectively. Accuracy evaluations and comparative studies prove that the proposed method has the capability of classifying multi-type road markings simultaneously and distinguishing similar road markings like dashed markings, zebra crossings, and stop lines in complex urban scenes.

Key words: MLS points clouds, road markings classification, graph structure, attention mechanism, road markings vectorization

中图分类号:

P307

方莉娜, 王爽, 赵志远, 付化胜, 陈崇成. 车载激光点云中交通标线自动分类与矢量化[J]. 测绘学报, 2021, 50(9): 1251-1265.

FANG Lina, WANG Shuang, ZHAO Zhiyuan, FU Huasheng, CHEN Chongcheng. Automatic classification and vectorization of road markings from mobile laser point clouds[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(9): 1251-1265.

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车载激光点云中交通标线自动分类与矢量化

Automatic classification and vectorization of road markings from mobile laser point clouds

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