A CNN model for predicting size of buried objects from GPR B-Scans
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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