Operationally Relevant Artificial Training for Machine Learning: Improving the Performance of Automated Target Recognition Systems

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.