Training perception models for mission-critical environments often runs into a data availability problem: the most important edge cases—hazards, intrusions, or system failures—are rare and difficult to capture in the real world.
Synthetic data generation allows our applied research teams to simulate these edge cases, varying environmental conditions, lighting, and target presentations to build robustness into the models.
However, synthetic data must be carefully validated against real-world domains to prevent the model from learning simulation artifacts. We use synthetic data as an accelerant for applied R&D, always anchoring the final evaluation in physical reality.