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Reliable training of convolutional neural networks for GPR-based buried threat detection using the Adam optimizer and batch normalization
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Paper Abstract

The ground penetrating radar (GPR) is a remote sensing technology that has been successfully used for detecting buried explosive threats. A large body of published research has focused on developing algorithms that automatically detect buried threats using data from GPR sensors. One promising class of algorithms for this purpose is convolutional neural networks (CNNs), however CNNs suffer from overfitting due to the limited and variable nature of GPR data. One solution to this problem is to use a validation dataset during training, however this excludes valuable labeled data from training. In this work we show that two modern techniques for training CNNs – Batch Normalization and the Adam Optimizer - substantially improve CNN performance and reduce overfitting when applied jointly. We also investigate and identify useful settings for several important CNN hyperparameters: l2 regularization, Dropout, and the learning rate schedule. We find that the improved CNN (a baseline CNN, plus all of our improvements) substantially outperforms two competing conventional detection algorithms.

Paper Details

Date Published: 10 May 2019
PDF: 10 pages
Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 1101206 (10 May 2019); doi: 10.1117/12.2519798
Show Author Affiliations
Steven Jacobson, Duke Univ. (United States)
Daniël Reichman, Duke Univ. (United States)
Joel Bjornstad, Duke Univ. (United States)
Leslie M. Collins, Duke Univ. (United States)
Jordan M. Malof, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 11012:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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