Interim results from a neural network 3-D automatic target recognition program
Recent results from the Artificial Neural VIsion Learning (ANVIL) program are presented. The focus of the ANVIL program is to apply neural network technologies to the air-to-surface 3D automatic target recognition (ATR) problem. The 3D 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 identification accuracy, a high detection rate, and 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 over 500 targets of five types and two classes. These targets varied in scale by up to -25%, varied in azimuth by up to 120 degrees, and varied in elevation by up to 10 degrees. The performance results are shown for targets with resolution ranging from 9 to 700 pixels on target. This work is being performed under contract to Wright Laboratory AAAT-1.
Out of print 07/14/2006.
This paper was published in SPIE Proceedings Vol. 1700