Improving the histogram of oriented gradient feature for threat detection in ground penetrating radar by implementing it as a trainable convolutional neural network

Jordan M. Malof, John Bralich, Daniël Reichman, Leslie M. Collins

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A large number of algorithms have been proposed for automatic buried threat detection (BTD) in ground penetrating radar (GPR) data. Convolutional neural networks (CNNs) have recently achieved groundbreaking results on many recognition tasks. This success is due, in part, to their ability to automatically infer effective data representations (i.e., features) using training data. This capability however results in a high capacity model (i.e., many free parameters) that is difficult to train, and more prone to overfitting, than models employing hand-crafted feature designs. This drawback is pronounced when training data is relatively scarce, as is the case with GPR BTD. In this work we propose to combine the relative advantages of hand-crafted features, and CNNs, by constructing CNN architectures that closely emulate successful hand-crafted feature designs for GPR BTD. This makes it possible to apply supervised training to traditional hand-crafted features, allowing them to adapt to the unique characteristics of the GPR BTD problem. Simultaneously, this approach yields a much lower capacity CNN model that incorporates substantial prior research knowledge, making the model much easier to train. We demonstrate the feasibility and effectiveness of this approach by designing a "neural" implementation of the popular histogram of oriented gradient (HOG) feature. The resulting neural HOG (NHOG) implementation is much smaller and easier to train than standard CNN architectures, and achieves superior detection performance compared to the un-trained HOG feature. In theory, neural implementations can be developed for many existing successful GPR BTD algorithms, potentially yielding similar benefits.

Original languageEnglish
Title of host publicationDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII
EditorsSteven S. Bishop, Jason C. Isaacs
PublisherSPIE
ISBN (Electronic)9781510617674
DOIs
StatePublished - 2018
EventDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII 2018 - Orlando, United States
Duration: Apr 16 2018Apr 18 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10628
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII 2018
Country/TerritoryUnited States
CityOrlando
Period04/16/1804/18/18

Keywords

  • buried threat detection
  • convolutional neural networks
  • deep learning
  • generalization
  • ground penetrating radar
  • histogram of oriented gradients
  • regularization

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