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Generative adversarial networks for ground penetrating radar in hand held explosive hazard detection
Author(s): Charlie Veal; Joshua Dowdy; Blake Brockner; Derek T. Anderson; John E. Ball; Grant Scott
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Paper Abstract

The identification followed by avoidance or removal of explosive hazards in past and/or present conflict zones is a serious threat for both civilian and military personnel. This is a challenging task as extreme variability exists with respect to the objects, their environment and emplacement context. A goal is the development of automatic, or human-in-the-loop, sensor technologies that leverage engineering theories like signal processing, data fusion and machine learning. Herein, we explore the detection of buried explosive hazards (BEHs) in handheld ground penetrating radar (HH-GPR) via convolutional neural networks (CNNs). In particular, we investigate the potential for generative adversarial networks (GANs) to impute new data based on limited and class imbalance labeled data. Unsupervised GANs are trained and assessed at a qualitative level and their outputs are explored in different ways to quantitatively help train a CNN classifier. Overall, we found encouraging qualitative results and a list of hurdles that need to be overcome before we anticipate quantitative improvements.

Paper Details

Date Published: 30 April 2018
PDF: 18 pages
Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 106280T (30 April 2018); doi: 10.1117/12.2307261
Show Author Affiliations
Charlie Veal, Mississippi State Univ. (United States)
Joshua Dowdy, Mississippi State Univ. (United States)
Blake Brockner, Mississippi State Univ. (United States)
Derek T. Anderson, Univ. of Missouri (United States)
John E. Ball, Mississippi State Univ. (United States)
Grant Scott, Univ. of Missouri (United States)


Published in SPIE Proceedings Vol. 10628:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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