Research Findings from University of Tokyo Update Understanding of Remote Sensing (Detecting Subsurface Voids From GPR Images by 3-D Convolutional Neural Network Using 2-D Finite Difference Time Domain Method)

摘要

By a News Reporter-Staff News Editor at Network Daily News – Investigators publish new report on remote sensing. According to news reporting out of Tokyo, Japan, by NewsRx editors, research stated, “In this article, an algorithm for detecting subsurface voids under the road from ground penetrating radar images is proposed.” Our news reporters obtained a quote from the research from University of Tokyo: “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.”