A high-performance deep-learning-based Ground Penetrating Radar classification approach with special focus on multi-class robustness

Publication date
2025-10-08
Document type
Forschungsartikel
Author
Pagers, Mats
Organisational unit
DOI
URI
Publisher
IEEE
Series or journal
IEEE access
ISSN
Periodical volume
13
Article ID
11196756
Peer-reviewed
✅
Part of the university bibliography
✅
Funding(s)
Language
English
Keyword
Ground penetrating radar
GPR
Deep learning
Utility detection
Cavity detection
Image preprocessing
Bilateral filtering
Abstract
Urbanization is increasing the presence of various utilities in the ground, while at the same time underground cavities and sinkholes pose a major threat to the civilization and structural integrity of the infrastructure. Ground Penetrating Radar B-scans enable the non-invasive and mobile analysis of the ground. While manual analysis methods are complex and labor-intensive, deep learning methods have shown potential for automated analysis of the scans. While existing models allow a binary classification, this study uses a novel deep learning-based approach to classify Ground Penetrating Radar B-scan radargrams into three subsurface categories (utilities, cavities, and intact zones) to account for the complexity of real-world subsurface conditions. A ResNet50 model is used with a novel edge preserving preprocessing step to enhance the quality of input data, with the aim to reduce noise in the radargrams while keeping important structural details unblurred. The model shows significant improvements when domain-specific filtering is applied. Balanced accuracy increased from 93.06 % to 96.00 %, setting a new benchmark for Ground Penetrating Radar-based multi-class classification. The results show that combining image enhancement with deep learning can improve the analysis of underground imagery. The method is easily reproducible and is applicable in real-world scenarios such as infrastructure maintenance, utility mapping or archaeological prospection.
Description
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Version
Published version
Access right on openHSU
Metadata only access