Effects of LiDAR Point Density on Extraction of Traffic Signs: A Sensitivity Study

Abstract

Datasets collected using light detection and ranging (LiDAR) technology often consist of dense point clouds. However, the density of the point cloud could vary depending on several different factors including the capabilities of the data collection equipment, the conditions in which data are collected, and other features such as range and angle of incidence. Although variation in point density is expected to influence the quality of the information extracted from LiDAR, the extent to which changes in density could affect the extraction is unknown. Understanding such impacts is essential for agencies looking to adopt LiDAR technology and researchers looking to develop algorithms to extract information from LiDAR. This paper focuses specifically on understanding the impacts of point density on extracting traffic signs from LiDAR datasets. The densities of point clouds are first reduced using stratified random sampling; traffic signs are then extracted from those datasets at different levels of point density. The precision and accuracy of the detection process was assessed at the different levels of point cloud density and on four different highway segments. In general, it was found that for signs with large panels along the approach on which LiDAR data were collected, reducing the point cloud density by up to 70% of the original point cloud had minimal impacts on the sign detection rates. Results of this study provide practical guidance to transportation agencies interested in understanding the tradeoff in price, quality, and coverage, when acquiring LiDAR equipment for the inventory of traffic signs on their transportation networks.