Abstract
The recent development of novel powerful sensor topologies, namely Ground Penetrating Radar (GPR) antennas, gave a thrust to the modeling of underground environment. An important step towards underground modelling is the detection of the typical hyperbola patterns on 2D images (B-scans), formulated due to the reflections of underground utilities. This work introduces a soil-agnostic approach for hyperbola detection, starting from one dimensional GPR signals, viz. A-scans, to perform a segmentation of each trace into candidate reflection pulses. Feature vector representations are calculated for segmented pulses through multilevel DWT decomposition. A theoretical geometric model of the corresponding hyperbola pattern is generated on the image plane for all point coordinates of the area under inspection. For each theoretical model, measured pulses that best support it are extracted and are utilized to validate it with a novel hybrid measure. The novel measure simultaneously controls the geometric plausibility of the examined hyperbola model and the consistency of the pulses contributing to this model across all the examined A-scan traces. Implementation details are discussed and experimental evaluation is exhibited on real GPR data.
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Acknowledgment
The work funded by the EU-H2020 funded project “BADGER (RoBot for Autonomous unDerGround trenchless opERations, mapping and navigation)” with GA no: 731968.
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Skartados, E., Kostavelis, I., Giakoumis, D., Tzovaras, D. (2019). Hybrid Geometric Similarity and Local Consistency Measure for GPR Hyperbola Detection. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_21
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