Pavement texture depth estimation using image-based multiscale features
Texture depth, a fundamental indicator for pavement performance, is traditionally obtained by time-consuming measurements. The image-based estimation has become a new trend due to its convenience and economy. This study applies image-based multiscale features for texture depth estimation. Maximum particle size distribution (MPSD) and relative energy distribution (RED) are proposed based on multiscale segmentation and 2D-wavelet decomposition. Two hundred fifty image samples labelled with electronic mean texture depth (eMTD) were collected. The multivariable nonlinear regressors are developed to deal with features' multicollinearity. As a result, the models where the input is the combination of MPSD and RED have better performances than those where the input is two sets of features. The random forest model yields the best results (cross-fold validation R² = 0.8192). The proposed method has the potential to enhance vision-based MTD measurements, which supports pavement quality evaluation during construction.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/09265805
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Supplemental Notes:
- © 2022 Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
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Authors:
- Weng, Zihang
- Xiang, Hui
- Lin, Yuchao
- Liu, Chenglong
- Wu, Difei
- Du, Yuchuan
- Publication Date: 2022-9
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: 104404
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Serial:
- Automation in Construction
- Volume: 141
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0926-5805
- Serial URL: http://www.sciencedirect.com/science/journal/09265805
Subject/Index Terms
- TRT Terms: Asphalt pavements; Image processing; Multivariate analysis; Pavement performance; Regression analysis; Texture
- Subject Areas: Highways; Pavements;
Filing Info
- Accession Number: 01855304
- Record Type: Publication
- Files: TRIS
- Created Date: Aug 22 2022 4:14PM