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 and learns additional low-level features when the input data passes through the neural network to finally make an estimation of the required parameter. Artificially generated GPR B-Scans are used to train the model. The proposed method demonstrates good performance in predicting buried object size. Upon comparison, Scharr operator followed by a deep CNN model showed the best performance, having the minimum mean absolute percentage error of 6.74 when tested on new, unseen data.