ODUSI: object detection using synthetic imagery

Robust and resilient machine learning is critical to leading the world in cutting-edge technology for defense, but to achieve it, we need large amounts of representative data. Unfortunately, collecting and labeling real world data can be expensive and time-consuming. Computer generated data, often referred to as synthetic data, has made it possible to exponentially increase the amount of labeled data available with methods of creation such as generative models. Despite this growing trend to dedicate money and resources to produce synthetic data via simulated environments, it remains undetermined if training algorithms on synthetic data is an advantage for mission critical object detection tasks. In this paper, we propose a unique data quality metric that will support or counter the hypothesis that synthetic data is a viable alternative to using real world data. This data quality metric will determine the viability of using "digital twins" to generate more controllable and diverse synthetic images to overcome the lack of training data that hinders targeting related algorithms such as Automated Target Recognition (ATR) and Battle Damage Assessment (BDA).