Automatic Detection and Classification of Underground Objects in Ground Penetrating Radar Images Using Machine Learning-Reference-Cited by-同舟云学术

Automatic Detection and Classification of Underground Objects in Ground Penetrating Radar Images Using Machine Learning

Published:2023-11 Issue:4 Volume:14 Page:
ISSN:1949-1190
Container-title:Journal of Pipeline Systems Engineering and Practice
language:en
Short-container-title:J. Pipeline Syst. Eng. Pract.

Author:

Amaral Leila Carolina Martoni¹,Roshan Aditya²,Bayat Alireza³

Affiliation:

1. Dept. of Civil and Environmental Engineering, Univ. of Alberta, 9211 116 St. NW, Edmonton, AB, Canada T6G 1H9.

2. Dept. of Civil and Environmental Engineering, Univ. of Alberta, 9211 116th St., Edmonton, AB, Canada T6G 1H9.

3. Professor, Dept. of Civil and Environmental Engineering, Univ. of Alberta, 9211 116th St., Edmonton, AB, Canada T6G 1H9 (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanical Engineering,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JPSEA2.PSENG-1444

Reference39 articles.

1. Automatic detection of hyperbolic signatures in ground-penetrating radar data

2. Machine Learning from Theory to Algorithms: An Overview

3. Amaral, L. C. M. 2021. Automatic detection of underground objects in ground penetrating radar images using machine learning. Edmonton, AB, Canada: Univ. of Alberta Libraries.

4. Review of Machine Learning Algorithms for Automatic Detection of Underground Objects in GPR Images

5. Arcand L. P. Eng and H. Osman. 2006. “Utilization of subsurface utility engineering to improve the effectiveness of utility relocation and coordination efforts on highway projects in Ontario.” In Proc. Annual Conf. of the Transportation Association of Canada Charlottetown 17–20. Ottawa: Transportation Association of Canada.

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