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

Steven Jacobson; Daniël Reichman; Joel Bjornstad; Leslie M. Collins; Jordan M. Malof

doi:10.1117/12.2519798

10 May 2019 Reliable training of convolutional neural networks for GPR-based buried threat detection using the Adam optimizer and batch normalization

Steven Jacobson, Daniël Reichman, Joel Bjornstad, Leslie M. Collins, Jordan M. Malof

Author Affiliations +

Proceedings Volume 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV; 1101206 (2019) https://doi.org/10.1117/12.2519798
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Conference Presentation

Citation Download Citation

Steven Jacobson, Daniël Reichman, Joel Bjornstad, Leslie M. Collins, and Jordan M. Malof "Reliable training of convolutional neural networks for GPR-based buried threat detection using the Adam optimizer and batch normalization", Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 1101206 (10 May 2019); https://doi.org/10.1117/12.2519798

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available