Translator Disclaimer
Presentation + Paper
10 May 2019 Reliable training of convolutional neural networks for GPR-based buried threat detection using the Adam optimizer and batch normalization
Author Affiliations +
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
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
PROCEEDINGS
10 PAGES + PRESENTATION

SHARE
Advertisement
Advertisement
Back to Top