In this article, an algorithm for detecting subsurface voids under the road from ground penetrating radar images is proposed. A multichannel radar system mounted on vehicle enables dense and highspeed… Click to show full abstract
In this article, an algorithm for detecting subsurface voids under the road from ground penetrating radar images is proposed. A multichannel radar system mounted on vehicle enables dense and highspeed monitoring. The novelty of the algorithm is a unique ElectroMagnetic simulation method and state-of-the-art deep learning technique to consider three-dimensional (3-D) reflection patterns of voids. To train deep learning models, 3-D reflection patterns were reproduced by 2-D finite difference time domain method to drastically reduce the calculation cost. Hyperboloid reflection patterns of voids were extracted by 3-D convolutional neural network (3D-CNN). The classification accuracy of 3D-CNN was up to $90$%, about 10% improvement compared to previous 2D-CNN to demonstrate the effectiveness of 3-D subsurface sensing and detection. The results were validated by real void measurement data. After applying trained 3D-CNN to radar data, regions of voids were plotted in a 3-D map, offering clear visualization of areas of voids.
               
Click one of the above tabs to view related content.