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

Award Information
Agency: Department of Defense
Branch: Army
Contract: W909MY-11-C-0016
Agency Tracking Number: A2-4403
Amount: $773,820.00
Phase: Phase II
Program: SBIR
Awards Year: 2011
Solicitation Year: 2009
Solicitation Topic Code: A09-078
Solicitation Number: 2009.2
Small Business Information
Signal Innovations Group, Inc.
1009 Slater Rd., Suite 200, Durham, NC, -
DUNS: 147201342
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Levi Kennedy
 Engineer 5
 (919) 323-3456
 lkennedy@siginnovations.com
Business Contact
 Samantha Venters
Title: VP Finance
Phone: (919) 323-3449
Email: sventers@siginnovations.com
Research Institution
 Stub
Abstract
Fully automated approaches to target detection are efficient at processing large amounts of data but often rely heavily on the training data and the model employed. Training data and modeling assumptions can be violated in the operational environments where the algorithms are applied. Alternatively, human manual target detection is accurate and adaptable due to the human ability to interpret data within its wider context. However, operational constraints and the overwhelming amount of data from modern sensors frequently preclude a fully manual approach to target detection. The SIG human-in-the-loop (HIL) active learning (AL) framework allows the operators to guide the training of the automated detection algorithm when the training and testing data statistics are mismatched. In this structured framework, the algorithm cues the operator using two specific criteria: detections that are high probability targets and detections that are highly informative for improving the classifier performance. The operator provides labels for the cued detections, and the new label information is used to retrain the classifier and improve the performance of the algorithm. At the conclusion of this Phase II effort, SIG will deliver a C/C++ implementation of the HIL/AL architecture that is ready for integration and test on the Shadow IED program.

* information listed above is at the time of submission.

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