Details
| Original language | English |
|---|---|
| Journal | Advances in Mechanical Engineering |
| Volume | 11 |
| Issue number | 9 |
| Early online date | 6 Sept 2019 |
| Publication status | Published - Sept 2019 |
Abstract
An automatic and intelligent method for crack detection is significantly important, considering the popularity of large constructions. How to identify the cracks intelligently from massive point cloud data has become increasingly crucial. Terrestrial laser scanning is a measurement technique for three-dimensional information acquisition which can obtain coordinates and intensity values of the laser reflectivity of a dense point cloud quickly and accurately. In this article, we focus on the optimal parameter of Gaussian filtering to balance the efficiency of crack identification and the accuracy of crack analysis. The innovation of this article is that we propose a novel view of the signal-to-noise ratio gradient for Gaussian filtering to identify and extract the cracks automatically from the point cloud data of the terrestrial laser scanning measurement.
Keywords
- automatic identification, crack extraction, Intelligent monitoring, terrestrial laser scanning, tunnel structures
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
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In: Advances in Mechanical Engineering, Vol. 11, No. 9, 09.2019.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Intelligent crack extraction and analysis for tunnel structures with terrestrial laser scanning measurement
AU - Xu, Xiangyang
AU - Yang, Hao
N1 - Funding information: The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The publication of this article was funded by the Open Access Fund of the Leibniz Universität Hannover. The authors gratefully acknowledge the support of the Natural Science Foundation of Jiangsu Province (grant no. BK20160558).
PY - 2019/9
Y1 - 2019/9
N2 - An automatic and intelligent method for crack detection is significantly important, considering the popularity of large constructions. How to identify the cracks intelligently from massive point cloud data has become increasingly crucial. Terrestrial laser scanning is a measurement technique for three-dimensional information acquisition which can obtain coordinates and intensity values of the laser reflectivity of a dense point cloud quickly and accurately. In this article, we focus on the optimal parameter of Gaussian filtering to balance the efficiency of crack identification and the accuracy of crack analysis. The innovation of this article is that we propose a novel view of the signal-to-noise ratio gradient for Gaussian filtering to identify and extract the cracks automatically from the point cloud data of the terrestrial laser scanning measurement.
AB - An automatic and intelligent method for crack detection is significantly important, considering the popularity of large constructions. How to identify the cracks intelligently from massive point cloud data has become increasingly crucial. Terrestrial laser scanning is a measurement technique for three-dimensional information acquisition which can obtain coordinates and intensity values of the laser reflectivity of a dense point cloud quickly and accurately. In this article, we focus on the optimal parameter of Gaussian filtering to balance the efficiency of crack identification and the accuracy of crack analysis. The innovation of this article is that we propose a novel view of the signal-to-noise ratio gradient for Gaussian filtering to identify and extract the cracks automatically from the point cloud data of the terrestrial laser scanning measurement.
KW - automatic identification
KW - crack extraction
KW - Intelligent monitoring
KW - terrestrial laser scanning
KW - tunnel structures
UR - http://www.scopus.com/inward/record.url?scp=85073077196&partnerID=8YFLogxK
U2 - 10.1177/1687814019872650
DO - 10.1177/1687814019872650
M3 - Article
AN - SCOPUS:85073077196
VL - 11
JO - Advances in Mechanical Engineering
JF - Advances in Mechanical Engineering
SN - 1687-8132
IS - 9
ER -