基于语义分割网络和特征匹配的复杂山区高速公路路面线性要素智能提取方法  

Intelligent extraction method for linear road surface features of complex mountainous highways based on semantic segmentation network and feature matching

作　　者：张开洲 马瑞峰 贾鑫 ZHANG Kaizhou;MA Ruifeng;JIA Xin(Gansu Natural Resources Information Center,Lanzhou 730030,China;School of Electronic Engineering,Chengdu Technological University,Chengdu 611730,China;Faculty of Geosciences and Engineering,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]甘肃省自然资源信息中心,甘肃兰州730030 [2]成都工业学院电子工程学院,四川成都611730 [3]西南交通大学地球科学与工程学院,四川成都611756

出　　处：《测绘通报》2025年第4期164-169,共6页Bulletin of Surveying and Mapping

基　　金：2024年甘肃省自然资源青年人才(团队)项目(202413)。

摘　　要：道路边界和标线作为关键路面线性要素,其准确位置和语义信息为高速公路改扩建和智能化资产管理提供了重要支撑。移动激光扫描(MLS)点云已成为道路场景要素识别及更新的重要数据源。本文针对现有方法在复杂山区高速公路场景中的不足,提出了一种车载激光点云高速公路路面线性要素智能提取由粗到精的方法:基于三维点云的深度学习网络对路面和标线点云进行语义分割,获取粗结果;进一步对路面边界和标线进行精提取与优化处理,包括路面轮廓点提取、线结构聚类、路面边界点补全及矢量化,并利用特征属性判断和基于边缘的特征匹配实现标线精细提取与语义化。试验结果表明,本文方法能够有效克服复杂山区高速公路场景中的遮挡和噪声问题,准确提取路面线性要素。Road boundaries and markings,as crucial linear elements of road surfaces,provide vital support for highway expansion,reconstruction,and intelligent asset management by offering accurate positioning and semantic information.Mobile LiDAR scanning(MLS)point clouds have emerged as a significant data source for identifying and updating road scene elements.Addressing shortcomings of existing methods in complex mountainous highway scenarios,this study proposes a progressive method for intelligent extraction of linear road surface features from vehicle-mounted LiDAR point clouds:Utilizing a deep learning network based on 3D point clouds,semantic segmentation of road surfaces and markings is performed to obtain coarse results.Subsequently,refined extraction and optimization processes are applied to road boundaries and markings.This includes extraction of road contour points,clustering of line structures,completion and vectorization of road boundary points,and semantic enhancement through feature attribute analysis and edge-based feature matching for precise extraction and semantic interpretation of markings.Experimental results demonstrate that the proposed method effectively addresses occlusion and noise challenges in complex mountainous highway scenes,achieving accurate extraction of linear road surface features.

关 键 词：MLS点云 语义分割网络 高速公路路面 标线 边缘匹配 

分 类 号：P237[天文地球—摄影测量与遥感]