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GPR-based detection and classification of railway infrastructure using near-surface features
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- Journal:
- International Journal of Microwave and Wireless Technologies , First View
- Published online by Cambridge University Press:
- 26 May 2026, pp. 1-9
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- Article
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This paper introduces a methodology for detecting and classifying railway infrastructure elements using ground-penetrating radar (GPR). Seven trips, covering
$57.4\,$km across a German track network were recorded. The network includes ballasted tracks, switches, bridges with dominant metal elements, and overpasses. GPR data from a multi-channel sensor array is transformed through a preprocessing pipeline to generate spatial B-scans. Statistical and structural features, including energy, depth, and skewness, are extracted via an overlapping sliding window. The detection algorithm operates as a streaming process, combining correlation-based pattern matching with feature-based thresholds in a state-machine architecture. The evaluation results demonstrate overall good performance for infrastructure detection and classification (
$98$–
$100\%$), with only minor misclassifications occurring for switch orientation estimation in scenarios involving closely spaced or physically interconnected switches (
$82$–
$100\%$).
