Title
A Component Decomposition Model for 3D Laser Scanning Pavement Data Based on High-Pass Filtering and Sparse Analysis.
Abstract
High-precision 3D laser scanning pavement data contains rich pavement scene information and certain components associations. Moreover, for pavement maintenance and management, there is an urgent need to develop automatic methods that can extract comprehensive information about different pavement indicators simultaneously. By analyzing the frequency and sparse characteristics of pavement distresses and performance indicators including the cracks, road markings, rutting, potholes, textures this paper proposes 3D pavement components decomposition model (3D-PCDM) which decomposes the 3D pavement profiles into sparse components x, low-frequency components f, and vibration components t. Designed high-pass filter was first employed to separate f, then, x and t are separated by total variation de-noising which based on sparse characteristics. Decomposed x can be used to characterize the location and depth information of sparse and sparse derived signals such as cracks, road marks, grooves, and potholes in profiles. Decomposed f can be used to determine the slow deformation of pavement. While decomposed t reflects the fluctuation of the pavement material particles. Experiments were conducted using actual pavement 3D data, the decomposed components can obtain by 3D-PCDM. The effectiveness and accuracy of the x are verified by actual cracks and road markings, the accuracy of extracted sparse components is over 92.75%.
Year
DOI
Venue
2018
10.3390/s18072294
SENSORS
Keywords
Field
DocType
3D laser scanning,components decomposition,pavement distresses and performance indicators,high-pass filtering,total variation de-noising
Laser scanning,Filter (signal processing),High-pass filter,Electronic engineering,Engineering,Decomposition
Journal
Volume
Issue
Citations 
18
7.0
0
PageRank 
References 
Authors
0.34
14
7
Name
Order
Citations
PageRank
Rong Gui1165.00
Xin Xu216240.08
Dejin Zhang300.68
Hong Lin400.68
Fangling Pu5357.24
Li He614.07
Min Cao701.01