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13 May 2019 Deep learning for automatic ordnance recognition
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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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chris M. Ward, Josh Harguess, Cameron Hilton, Chelsea Mediavilla, Keith Sullivan, and Rick Watkins "Deep learning for automatic ordnance recognition", Proc. SPIE 10992, Geospatial Informatics IX, 109920H (13 May 2019);

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