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Proceedings Paper

Deep learning for automatic ordnance recognition
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Paper Abstract

Explosive Ordnance Disposal (EOD) technicians are on call to respond to a wide variety of military ordnance. As experts in conventional and unconventional ordnance, they are tasked with ensuring the secure disposal of explosive weaponry. Before EOD technicians can render ordnance safe, the ordnance must be positively identified. However, identification of unexploded ordnance (UXO) in the field is made difficult due to a massive number of ordnance classes, object occlusion, time constraints, and field conditions. Currently, EOD technicians collect photographs of unidentified ordnance and compare them to a database of archived ordnance. This task is manual and slow - the success of this identification method is largely dependent on the expert knowledge of the EOD technician. In this paper, we describe our approach to automatic ordnance recognition using deep learning. Since the domain of ordnance classification is unique, we first describe our data collection and curation efforts to account for real-world conditions, such as object occlusion, poor lighting conditions, and non-iconic poses. We apply a deep learning approach using ResNet to this problem on our collected data. While the results of these experiments are quite promising, we also discuss remaining challenges and potential solutions to deploying a real system to assist EOD technicians in their extremely challenging and dangerous role.

Paper Details

Date Published: 13 May 2019
PDF: 10 pages
Proc. SPIE 10992, Geospatial Informatics IX, 109920H (13 May 2019); doi: 10.1117/12.2519241
Show Author Affiliations
Chris M. Ward, Naval Information Warfare Ctr. Pacific (United States)
Josh Harguess, Naval Information Warfare Ctr. Pacific (United States)
Cameron Hilton, Naval Information Warfare Ctr. Pacific (United States)
Chelsea Mediavilla, Naval Information Warfare Ctr. Pacific (United States)
Keith Sullivan, U.S. Naval Research Lab. (United States)
Rick Watkins, Naval Surface Warfare Ctr. Indian Head Div. (United States)


Published in SPIE Proceedings Vol. 10992:
Geospatial Informatics IX
Kannappan Palaniappan; Peter J. Doucette; Gunasekaran Seetharaman, Editor(s)

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