Deep Learning Research by NSWC Crane Engineer to Help Electronic Warfare Capabilities

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July 25, 2019 | Originally published by Date Line: July 25 on

CRANE, Indiana. – Research on how to leverage artificial intelligence (AI) for electronic warfare applications was completed by a Naval Surface Warfare Center, Crane Division (NSWC Crane) employee.

David Emerson, a NSWC Crane engineer, defended his research that makes use of deep learning to process images and determine distances between objects in a scene. “Self-driving cars are an example of such a system, as they use deep learning to reconstruct scenes in 3-D,” said Emerson. But he explained there is interference that influences the effectiveness of currently used methodology, and depth estimation is one of the most challenging problems in computer vision.

“The human eye can see and understand depth in a scene,” Emerson explained. “A computer sees 2-D images and has to calculate the distance to reach an ‘understanding’ of depth between objects. In my Depth from Defocus method, I took one photo in focus and one photo out of focus and constructed a 3-D image.”

Emerson’s Depth from Defocus (DfD) using deep learning (DL) methodology outperformed previously used methods, increasing the speed of the process, and was more robust in its handling of images in low-lighting conditions, according to an NSWC Crane release.

“In the military, technology is used to determine long distances in the field,” he said. “DfD and deep learning methodology has potential future applications that could considerably improve the Warfighter’s speed and capability, all while remaining stealthy.”

Emerson’s research was partially funded first with a Department of Defense (DoD) Science, Mathematics And Research for Transformation (SMART) Scholarship for Service Program and then later with full sponsorship by the NSWC Crane Ph.D. Fellowship Program.