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Optical Engineering

ANVIL neural network program for three-dimensional automatic target recognition
Author(s): William Thoet; Timothy G. Rainey; Dean W. Brettle; Lee A. Slutz; Fred Weingard
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Paper Abstract

The focus of the artificial neural vision learning (ANVIL) program is to apply neural network technologies to the air-to-surface 3-D automatic target recognition (ATR) problem. The 3-D multiple object detection and location system (MODALS) neural network was developed under the ANVIL program to simultaneously detect, locate, segment, and identify multiple targets. The performance results show a very high dentification accuracy, a high detection rate, and a low false alarm rate, even for areas with high clutter and shadowing. The results are shown as detection/false alarm curves and identification/false alarm curves. In addition, positional detection accuracy is shown for various scale sizes. To provide data for the program, visible terrain board imagery was collected under a variety of background and lighting conditions. Tests were made on more than 500 targets of five types and two classes. These targets varied in scale by up to − 25%, varied in azimuth by up to 120 deg, and varied in elevation by up to 10 deg. The performance results are shown for targets with resolution ranging from 9 to 700 pixels on target.

Paper Details

Date Published: 1 December 1992
PDF: 8 pages
Opt. Eng. 31(12) doi: 10.1117/12.60008
Published in: Optical Engineering Volume 31, Issue 12
Show Author Affiliations
William Thoet, Booz, Allen and Hamilton Inc. (United States)
Timothy G. Rainey, Booz-Allen and Hamilton Inc. (United States)
Dean W. Brettle, Booz, Allen and Hamilton Inc. (United States)
Lee A. Slutz, Booz, Allen and Hamilton Inc. (United States)
Fred Weingard, Booz, Allen and Hamilton Inc. (United States)

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