Review of Machine Learning Algorithms for Automatic Detection of Underground Objects in GPR Images-Reference-Cited by-同舟云学术

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

Published:2022-05 Issue:2 Volume:13 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. Graduate Student, Dept. of Civil and Environmental Engineering, Univ. of Alberta, 9211 116 St. NW, Edmonton, AB, Canada T6G 1H9.

2. Postdoctoral Fellow, 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

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29PS.1949-1204.0000632

Reference63 articles.

1. Handwriting Arabic character recognition LeNet using neural network;Al-Jawfi R.;Int. Arab J. Inf. Technol.,2009

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

3. Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition

4. Ardekani S. 2006. “Automatic and fast detection of buried utilities positions and estimation of soil permittivity using GPR.” In Proc. 11th Int. Conf. on Ground Penetrating Radar. Columbus OH: Ohio State Univ.

5. Comparison of backpropagation and resilient backpropagation algorithms in non-invasive blood glucose measuring device;Avan E.;Int. J. Sci. Eng. Res.,2017

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. SUITABILITY ASSESSMENT OF DIFFERENT SENSORS TO DETECT HIDDEN INSTALLATIONS FOR AS-BUILT BIM;ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences;2023-12-05

2. Automatic Detection and Classification of Underground Objects in Ground Penetrating Radar Images Using Machine Learning;Journal of Pipeline Systems Engineering and Practice;2023-11

3. End-to-end deep learning model for underground utilities localization using GPR;Automation in Construction;2023-05

4. Incremental constraint-based reasoning for estimating as-built electric line routing in buildings;Automation in Construction;2022-11