Automated target recognition (ATR) is an important potential U.S. military application among the many recent advances in artificial intelligence and machine learning. An obstacle to creating a successful ATR system with machine learning is the collection of high-quality labeled data sets. The authors explored whether this obstacle could be sidestepped by training object-detection algorithms on high-resolution, realistic artificial images.
