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

author：

Fang, Lina (Fang, Lina.) ^[1] (Scholars：方莉娜) | Wang, Shuang (Wang, Shuang.) ^[2] | Zhao, Zhiyuan (Zhao, Zhiyuan.) ^[3] (Scholars：赵志远) | Fu, Huasheng (Fu, Huasheng.) ^[4] | Chen, Chongcheng (Chen, Chongcheng.) ^[5] (Scholars：陈崇成)

Indexed by：

EI PKU

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.　©　2021,　Surveying　and　Mapping　Press.　All　right　reserved.

Keyword：

Classification (of information) Graphic methods Highway markings Road and street markings Roads and streets Topology

Community：

[ 1 ] [Fang, Lina]National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou; 350002, China
[ 2 ] [Fang, Lina]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou; 350002, China
[ 3 ] [Fang, Lina]Academy of Digital China, Fuzhou University, Fuzhou; 350002, China
[ 4 ] [Wang, Shuang]National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou; 350002, China
[ 5 ] [Wang, Shuang]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou; 350002, China
[ 6 ] [Wang, Shuang]Academy of Digital China, Fuzhou University, Fuzhou; 350002, China
[ 7 ] [Zhao, Zhiyuan]National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou; 350002, China
[ 8 ] [Zhao, Zhiyuan]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou; 350002, China
[ 9 ] [Zhao, Zhiyuan]Academy of Digital China, Fuzhou University, Fuzhou; 350002, China
[ 10 ] [Fu, Huasheng]Fujian Provincial Investigation, Design & Research Institute of Water Conservancy & Hydropower, Fuzhou; 350002, China
[ 11 ] [Chen, Chongcheng]National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou; 350002, China
[ 12 ] [Chen, Chongcheng]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou; 350002, China
[ 13 ] [Chen, Chongcheng]Academy of Digital China, Fuzhou University, Fuzhou; 350002, China