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%.
Estimation of soil moisture from GPR data using Artificial Neural Networks Read More »
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
A CNN model for predicting size of buried objects from GPR B-Scans Read More »
Abstract Traditional methods for classification of soil types are time consuming, invasive and expensive. A non-invasive method like ground penetrating radar (GPR) provides a suitable way to classify soil types based on its electromagnetic properties. Deep learning algorithms have proven to be an effective tool for features extraction of GPR data. A deep convolutional neural
Classification of Soil Types from GPR B-Scans using Deep Learning Techniques Read More »
