A Component Decomposition Model for 3D Laser Scanning Pavement Data Based on High-Pass Filtering and Sparse Analysis
Abstract
:1. Introduction
- The 3D-PCDM method decomposes the 3D laser scanning pavement profiles into sparse components, low-frequency components, and vibration components by employing the frequency characteristics and sparse characteristics of pavement distresses and performance indicators.
- 3D-PCDM provides a method basis for obtaining different types of information about pavement distresses and performance indicators simultaneously, the decomposed components can all be related to typical real interested pavement defects and performance indicators.
2. 3D Laser Scanning Pavement Data Acquisition and the Components Modeling
2.1. 3D Laser Scanning Pavement Data Acquisition
2.2. Component Characteristic Analysis in 3D Pavement Data
2.3. Total Variation De-Noising (Sparse Analysis)
3. Proposed 3D-PCDM
3.1. Component Frequency Information Acquisition
3.2. Low Frequency Component Separation
3.3. Sparse Component Separation Based on Total Variation De-Noising
3.4. Validation Method of Components Effectiveness
4. Experimental Results and Discussion
4.1. 3D Pavement Data Decomposition Results
4.2. Evaluation of Components Effectiveness
4.3. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Gui, R.; Xu, X.; Zhang, D.; Lin, H.; Pu, F.; He, L.; Cao, M. A Component Decomposition Model for 3D Laser Scanning Pavement Data Based on High-Pass Filtering and Sparse Analysis. Sensors 2018, 18, 2294. https://doi.org/10.3390/s18072294
Gui R, Xu X, Zhang D, Lin H, Pu F, He L, Cao M. A Component Decomposition Model for 3D Laser Scanning Pavement Data Based on High-Pass Filtering and Sparse Analysis. Sensors. 2018; 18(7):2294. https://doi.org/10.3390/s18072294
Chicago/Turabian StyleGui, Rong, Xin Xu, Dejin Zhang, Hong Lin, Fangling Pu, Li He, and Min Cao. 2018. "A Component Decomposition Model for 3D Laser Scanning Pavement Data Based on High-Pass Filtering and Sparse Analysis" Sensors 18, no. 7: 2294. https://doi.org/10.3390/s18072294