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A Component Decomposition Model for 3D Laser Scanning Pavement Data Based on High-Pass Filtering and Sparse Analysis.

Rong Gui1,2, Xin Xu3, Dejin Zhang4

  • 1School of Electronic Information, Wuhan University, Wuhan 430072, China. ronggui2013@whu.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a 3D Pavement Components Decomposition Model (3D-PCDM) to automatically extract pavement distresses like cracks and road markings from 3D laser scanning data, achieving over 92.75% accuracy.

Keywords:
3D laser scanningcomponents decompositionhigh-pass filteringpavement distresses and performance indicatorstotal variation de-noising

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Area of Science:

  • Civil Engineering
  • Geospatial Data Analysis
  • Pavement Engineering

Background:

  • High-precision 3D laser scanning provides rich pavement data.
  • Automatic extraction of pavement indicators is crucial for maintenance and management.
  • Existing methods struggle to extract multiple pavement indicators simultaneously.

Purpose of the Study:

  • To propose and validate a 3D Pavement Components Decomposition Model (3D-PCDM).
  • To enable simultaneous extraction of various pavement indicators from 3D data.
  • To enhance pavement maintenance and management through accurate data analysis.

Main Methods:

  • Analyzing frequency and sparse characteristics of pavement distresses and performance indicators.
  • Decomposing 3D pavement profiles into sparse (x), low-frequency (f), and vibration (t) components.
  • Employing a high-pass filter for f separation and total variation de-noising for x and t separation.

Main Results:

  • The 3D-PCDM successfully decomposes 3D pavement profiles into distinct components.
  • Decomposed sparse component (x) accurately characterizes location and depth of cracks and road markings.
  • Extracted sparse components achieved an accuracy exceeding 92.75% in experimental validation.

Conclusions:

  • The 3D-PCDM is effective for comprehensive pavement information extraction.
  • The model enables accurate identification of pavement distresses and deformations.
  • This approach significantly advances automated pavement assessment and management.