Abstract GPR (Ground Penetrating Radar) is a robust and effective device for identifying underground artefacts. Construction companies and civil engineers should be aware of the sizes of rebars and pipelines before and during construction work for various reasons. Most research efforts have typically concentrated on GPR signal analysis in the time domain, however recent studies…
A deep ANN trained on gprMax-simulated A-scans estimates soil moisture from GPR data with R² = 0.998 and a MAPE of 1.23%…
Abstract A convolutional neural networks (CNN) model for predicting size of buried objects from ground penetrating radar (GPR) B-Scans is proposed. As a pre-processing step, Sobel, Laplacian, Scharr, and Canny operators are used for edge detection of the hyperbolic features. The proposed CNN architecture extracts high level signatures in the initial stages of the model…
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