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

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$69,982.00
Award Year:
2010
Program:
SBIR
Phase:
Phase I
Contract:
W15P7T-10-C-S007
Agency Tracking Number:
A092-078-0679
Solicitation Year:
n/a
Solicitation Topic Code:
Army 09-078
Solicitation Number:
n/a
Small Business Information
Signal Innovations Group, Inc.
1009 Slater Rd., Suite 200, Durham, NC, 27703
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
147201342
Principal Investigator:
Levi Kennedy
Vice President of Engineering
(919) 323-3456
lkennedy@siginnovations.com
Business Contact:
David Dye
VP of Operations
(919) 794-3322
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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