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Material Classification of Underground Objects From GPR Recordings Using Deep Learning Approach

Exploration and detection of underground objects without excavation can be achieved by utilizing ground penetrating radar. Since such an approach is nondestructive, electromagnetic radiation has been used in order to accomplish sub-surface surveying. The correct interpretation of acquired ground penetrating radar data can be demanding, time-consuming and very challenging especially when the observed environment is noisy. However, with the assistance of artificial intelligence algorithms, such data can be processed and analyzed at high speed and with high accuracy. The aim of this research was to develop a deep learning model for the material classification of underground objects from ground penetrating radar recordings. Within the recordings, the pipes are usually visually represented as hyperbola-shaped features of different characteristics. Annotated by experts and preprocessed ground penetrating radar recordings were used as input to the deep convolutional neural networks. After the model was fully trained, performance on the validation set showed that the developed model can be used for pipe material classification from ground penetrating radar recordings with satisfactory results.

ground penetrating radar ; artificial intelligence ; deep learning ; convolutional neural network ; classification

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