Some good practices for applying convolutional neural networks to buried threat detection in Ground Penetrating Radar

Published

Conference Paper

© 2017 IEEE. Ground Penetrating Radar (GPR) is a remote sensing modality that has been researched extensively for buried threat detection. For this purpose, algorithms can be developed to automatically determine the presence of such threats. To train such algorithms, small 2-dimensional images can be extracted from the larger image, or volume, of GPR data. One thread of research in the buried threat detection literature is to use visual descriptors from the computer vision literature. One recent, very successful approach in that field is the use of deep convolutional neural networks (CNNs). Applying CNNs requires a large number of design choices which complicate their use. In this work, we investigate their application to GPR data and adapt several recent advances from the CNN literature to improve detection performance on GPR data. In particular, we investigate the initialization step of pretraining and propose a dataset augmentation protocol. The efficacy of these approaches are evaluated on several architectures with a relatively similar number of network parameters to learn. The results indicate that both pretraining and dataset augmentation help achieve higher detection performance.

Full Text

Duke Authors

Cited Authors

  • Reichman, D; Collins, LM; Malof, JM

Published Date

  • July 28, 2017

Published In

  • 2017 9th International Workshop on Advanced Ground Penetrating Radar, Iwagpr 2017 Proceedings

International Standard Book Number 13 (ISBN-13)

  • 9781509054848

Digital Object Identifier (DOI)

  • 10.1109/IWAGPR.2017.7996100

Citation Source

  • Scopus