Convolutional neural network based side attack explosive hazard detection in three dimensional voxel radar

Blake Brockner; Charlie Veal; Joshua Dowdy; Derek T. Anderson; Kathryn Williams; Robert Luke; David Sheen

doi:10.1117/12.2304507

30 April 2018 Convolutional neural network based side attack explosive hazard detection in three dimensional voxel radar

Blake Brockner, Charlie Veal, Joshua Dowdy, Derek T. Anderson, Kathryn Williams, Robert Luke, David Sheen

Author Affiliations +

Proceedings Volume 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII; 106281H (2018) https://doi.org/10.1117/12.2304507
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

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 variability exists with respect to the objects, their environment and emplacement context, to name a few factors. A goal is the development of automatic or human-in-the-loop sensor technologies that leverage signal processing, data fusion and machine learning. Herein, we explore the detection of side attack explosive hazards (SAEHs) in three dimensional voxel space radar via different shallow and deep convolutional neural network (CNN) architectures. Dimensionality reduction is performed by using multiple projected images versus the raw three dimensional voxel data, which leads to noteworthy savings in input size and associated network hyperparameters. Last, we explore the accuracy and interpretation of solutions learned via random versus intelligent network weight initialization. Experiments are provided on a U.S. Army data set collected over different times, weather conditions, target types and concealments. Preliminary results indicate that deep learning can perform as good as, if not better, than a skilled domain expert, even in light of limited training data with a class imbalance.

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Blake Brockner, Charlie Veal, Joshua Dowdy, Derek T. Anderson, Kathryn Williams, Robert Luke, and David Sheen "Convolutional neural network based side attack explosive hazard detection in three dimensional voxel radar", Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 106281H (30 April 2018); https://doi.org/10.1117/12.2304507

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