CONTEXT-DRIVEN LANDMINE DETECTION USING SEMI-SUPERVISED MULTI-TASK LEARNING

Award Information
Agency: Department of Defense
Branch: Army
Contract: W15P7T-10-C-S007
Agency Tracking Number: A092-078-0679
Amount: $69,982.00
Phase: Phase I
Program: SBIR
Awards Year: 2010
Solicitation Year: 2009
Solicitation Topic Code: A09-078
Solicitation Number: 2009.2
Small Business Information
1009 Slater Rd., Suite 200, Durham, NC, 27703
DUNS: 147201342
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Levi Kennedy
 Vice President of Engineering
 (919) 323-3456
 lkennedy@siginnovations.com
Business Contact
 David Dye
Title: VP of Operations
Phone: (919) 794-3322
Email: demetri@solidstateresearch.com
Research Institution
N/A
Abstract
The proliferation of landmines continues to be a problem of worldwide humanitarian urgency. While airborne sensors have demonstrated significant utility in covering a wide area at a high stand-off distance, the variety of deployment methods, environments, mine types, and operating conditions continue to pose challenges in the context of landmine detection requirements. In this effort, a context-driven approach to the landmine detection architecture is proposed with a focus on transitioning new mathematical and statistical tools for applied image processing that are highly relevant to addressing landmine detection challenges. We propose a framework that provides: texture-based context for anomaly detection; false alarm reduction through semi-supervised learning and multi-task learning where data associations are learned in the feature space and the classifier parameter space; mine field association through graph-based diffusion; and a mechanism to explicitly incorporate analyst feedback to optimize performance under new operating conditions. Computational efficiency of the underlying methods will aid in pursuing real-time implementation, test and evaluation in Phase II.

* Information listed above is at the time of submission. *

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