Paper Info
Title
Capsule-Based Networks for Road Marking Extraction and Classification From Mobile LiDAR Point Clouds
Abstract
Accurate road marking extraction and classification play a significant role in the development of autonomous vehicles (AVs) and high-definition (HD) maps. Due to point density and intensity variations from mobile laser scanning (MLS) systems, most of the existing thresholding-based extraction methods and rule-based classification methods cannot deliver high efficiency and remarkable robustness. To address this, we propose a capsule-based deep learning framework for road marking extraction and classification from massive and unordered MLS point clouds. This framework mainly contains three modules. Module I is first implemented to segment road surfaces from 3D MLS point clouds, followed by an inverse distance weighting (IDW) interpolation method for 2D georeferenced image generation. Then, in Module II, a U-shaped capsule-based network is constructed to extract road markings based on the convolutional and deconvolutional capsule operations. Finally, a hybrid capsule-based network is developed to classify different types of road markings by using a revised dynamic routing algorithm and large-margin Softmax loss function. A road marking dataset containing both 3D point clouds and manually labeled reference data is built from three types of road scenes, including urban roads, highways, and underground garages. The proposed networks were accordingly evaluated by estimating robustness and efficiency using this dataset. Quantitative evaluations indicate the proposed extraction method can deliver 94.11% in precision, 90.52% in recall, and 92.43% in F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score, respectively, while the classification network achieves an average of 3.42% misclassification rate in different road scenes.
Year
DOI
Venue
2021
10.1109/TITS.2020.2990120
IEEE Transactions on Intelligent Transportation Systems
Keywords
DocType
Volume
Point cloud,LiDAR,capsule network,road surface,road marking,extraction,classification,dynamic routing
Journal
22
Issue
ISSN
Citations 
4
1524-9050
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Lingfei Ma1286.87
Ying Li2395.02
Jonathan Li3798119.18
Yongtao Yu411614.05
Jose Marcato Junior501.35
Wesley Nunes Gonçalves61211.51
Michael Chapman7906.40