Synthetic UAV data validity depends on real task and models
Can Synthetic UAV Benchmarks Be Trusted? Real-Synthetic Validation for UAV Model Selection
Computer Vision and Pattern RecognitionRobotics
Summary
Synthetic images for drone tasks can help when real images are hard to get, but they aren’t always reliable to pick the best AI model. The authors tested two different ways to evaluate drone models using synthetic data. Sometimes the synthetic tests matched real-life results, but other times they picked a different best model. They found synthetic data often misses small objects and the overall mix of things seen by real drones. This means synthetic data alone can’t be trusted without careful checks for each use case.
What this means in practice
- •For drone software developers: Evaluate if synthetic UAV data can reliably help select perception models before real-world deployment under specific task conditions.
- •For drone operations teams: Use empirical checks of synthetic benchmarks to validate software model choices for different operational scenarios to reduce testing risks.
Authors
Yuyi Zhi, Min Du, Bill Shi, Guodong Yuan, Junheng Liang, Peifan Liao, Xin Guan
Abstract
Synthetic UAV imagery can expand pre-deployment test evidence when task-specific Real data are scarce, but visual plausibility or semantic label alignment alone does not establish validity for model selection. We study when Synthetic evaluation preserves the model-selection conclusions obtained from Real UAV data through two complementary cases under fixed evaluation protocols. In a requirement-specific road-debris benchmark, four language-conditioned perception systems obtain lower absolute F1 on Synthetic data, yet the point-estimate ordering and top-ranked model are preserved; bootstrap analysis yields no supported pairwise reversal, and size-matched resampling preserves the top-model decision. In contrast, a closed-set stress test on a separately materialized business corpus, using four VisDrone-compatible detectors over eight empirically supported classes, changes the point-estimate top-ranked detector and reverses three of six pairwise point-estimate preference directions. A complementary audit of five UAV business scenarios further reveals substantial Real-Synthetic mismatch in class composition, object density, and target scale, including systematic under-representation of small targets. These results show that Synthetic UAV benchmark trust is conditional: semantic coverage is insufficient, and validity must be established empirically for the task, operating regime, and model panel of interest.