Ground penetrating radar (GPR) is a typical sensor system for underground objects detection area. The multichannel GPR devices can give more detail and informative three-dimensional (3D) data for classification underground objects. Spatial information of underground objects can be well characterized in the three-dimensional GPR block data which consists of several B-scan and C-scan data. In this article underground object classification method is proposed by using 3D GRP data. Deep learning technique is recently being adopted into this field due to its powerful image classification capacity. The 3D GRP block data is then used to train deep three-dimensional convolution neural network (3D CNN). The proposed method successfully classifies cavity, pipe, manhole and subsoils having small false positive errors. The suggested method is experimentally validated by area data collected on urban roads in Seoul, South Korea.

Abstract
 1. Introduction
 2. Underground void detection with 3D GPR block data using three-dimensionalconvolution neural network
  2.1 3D GPR data acquisition and interpretation of 3D GPR data
  2.2 Proposed method for underground void detection
 3. Experimental validation of proposed method
 3. Conclusion

• 이종재(세종대학교 건설환경공학과) | Lee Jong-Jae
• Shekhroz Khudoyarov(세종대학교 건설환경공학과)