Paper Info
Title
A graph attention network for road marking classification from mobile LiDAR point clouds
Abstract
The category of road marking is a crucial element in Mobile laser scanning systems' (MLSs) applications such as intelligent traffic systems, high-definition maps, location and navigation services. Due to the complexity of road scenes, considerable and various categories, occlusion and uneven intensities in MLS point clouds, finely road marking classification is considered as the challenging work. This paper proposes a graph attention network named GAT_SCNet to simultaneously group the road markings into 11 categories from MLS point clouds. Concretely, the proposed GAT_SCNet model constructs serial computable subgraphs and fulfills a multi-head attention mechanism to encode the geometric, topological, and spatial relationships between the node and neighbors to generate the distinguishable descriptor of road marking. To assess the effectiveness and general-ization of the GAT_SCNet model, we conduct extensive experiments on five test datasets of about 100 km in total captured by different MLS systems. Three accuracy evaluation metrics: average Precision, Recall, and F-1 of 11 categories on the test datasets exceed 91%, respectively. Accuracy evaluations and comparative studies show that our method has achieved a new state-of-the-art work on road marking classification, especially on similar linear road markings like stop lines, zebra crossings, and dotted lines.
Year
DOI
Venue
2022
10.1016/j.jag.2022.102735
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
Keywords
DocType
Volume
MLS points clouds, Road marking classification, Graph neural network, Attention mechanism, Deep learning
Journal
108
ISSN
Citations 
PageRank 
1569-8432
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Lina Fang100.34
Tongtong Sun200.34
Shuang Wang300.34
Hongchao Fan4177.44
Jonathan Li5798119.18