Review of Machine Learning Algorithms for Automatic Detection of Underground Objects in GPR Images
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 13, Issue 2
Abstract
Ground-penetrating radar (GPR) is a nondestructive tool that has gained popularity after giving promising results in different areas—such as utility engineering, transportation engineering, civil engineering, and geology—with relatively low cost. Even as the number of applications for GPR increases, the interpretation of GPR data is still challenging, in part due to varying ground conditions. Researchers are continuously working on the development of new analysis methods to address these challenges. Computer vision algorithms, including neural networks and convolution neural networks, have advanced significantly over the past decade, and researchers have utilized these algorithms to extract information from GPR images and thus improve the interpretation of GPR data. This paper presents a review of literature that employs computer vision and machine learning algorithms, such as YOLO V3, Viola–Jones, and AlexNet, for automatic extraction of information from GPR images. The uptake in the use of automatic detection algorithms for GPR is increased by the ability to rapidly quantify and locate buried targets that previously could only be identified by professionals with a high level of expertise and training.
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Data Availability Statement
No data, models, or code were generated or used during the study (e.g., opinion or data-less paper).
Acknowledgments
The authors would like to thank Lana Gutwin of the Consortium of Engineered Trenchless Technologies (CETT) at the University of Alberta for her assistance with editing this manuscript. Funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) is gratefully acknowledged.
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Journal of Pipeline Systems Engineering and Practice
Volume 13 • Issue 2 • May 2022
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© 2021 American Society of Civil Engineers.
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Received: Jun 9, 2021
Accepted: Nov 3, 2021
Published online: Dec 23, 2021
Published in print: May 1, 2022
Discussion open until: May 23, 2022
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Cited by
- Leila Carolina Martoni Amaral, Aditya Roshan, Alireza Bayat, Automatic Detection and Classification of Underground Objects in Ground Penetrating Radar Images Using Machine Learning, Journal of Pipeline Systems Engineering and Practice, 10.1061/JPSEA2.PSENG-1444, 14, 4, (2023).
- Yang Su, Jun Wang, Danqi Li, Xiangyu Wang, Lei Hu, Yuan Yao, Yuanxin Kang, End-to-end deep learning model for underground utilities localization using GPR, Automation in Construction, 10.1016/j.autcon.2023.104776, 149, (104776), (2023).
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