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Airborne Video Inspection for Automatic Targeting with Ontology Reasoning (AVIATOR)

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8750-19-C-0043
Agency Tracking Number: F18B-007-0088
Amount: $149,981.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF18B-T007
Solicitation Number: 18.B
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-01-22
Award End Date (Contract End Date): 2019-01-22
Small Business Information
625 Mount Auburn Street
Cambridge, MA 02138
United States
DUNS: 115243701
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Ross Eaton
 Senior Scientist, Division Director
 (617) 491-3474
Business Contact
 Yvonne Fuller
Phone: (617) 491-3474
Research Institution
 Boston University
 Paul Murphy, JD Paul Murphy, JD
881 Commonwealth Avenue
Boston, MA 02215
United States

 (617) 358-1458
 Nonprofit College or University

Improved cameras and unmanned air vehicles (UAVs) have led to an explosion in the amount of airborne imagery and video collected by Air Force assets, but there are not enough trained personnel to analyze this imagery in real time. Thus, targets of opportunity and threats go undetected until the chance to act on them has passed. Automatic target detection could alleviate the burden on analysts and reduce the time required to detect and prosecute targets of opportunity. Deep learning techniques have revolutionized computer vision and established state of the art results in detection and classification. However, deep neural networks (DNNs) are black boxes of linear algebra that make inscrutable decisions. To bring the power of deep learning to bear on airborne image exploitation in a way that humans can understand and trust, we propose Airborne Video Inspection for Automatic Targeting with Ontology Reasoning (AVIATOR). AVIATOR uses deep learning to maximize airborne detection accuracy, but critically it uses a “bolt-on� explanation system that analyzes a DNN to provide end users with intuitive explanations for its decisions via a web-based graphical interface. These accurate human-understandable detections will gain the trust of users and support after action reviews to facilitate adoption.

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

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