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.