A UAV Video Data Generation Framework for Improved Robustness of UAV Detection Methods

Abstract

Recent advances have facilitated the development and popularization of Unmanned Aerial Vehicles (UAVs) that can operate semi or fully autonomously. The real-time, accurate visual detection of UAVs is crucial for various tasks and applications including surveillance (e.g., detecting UAVs flying over restricted areas such as airports) or multi-robot systems (e.g., a swarm of UAVs that need to cooperate and avoid collisions between swarm members in GPS-denied environments). The small target-to-image ratio and large similarity with other flying objects makes the visual detection of UAVs a challenging task. In addition, data distribution shifts can have a major negative impact to UAV detection frameworks, often trained on a wide variety of datasets to achieve an adequate level of robustness. As an attempt to mitigate the effect of these issues, we present a method that can generate realistic annotated video data depicting flying UAVs, using as input real background videos and 3D UAV models. The conducted experimental evaluation showed that the synthetic data are both challenging and realistic and that detectors trained on a combination of real-world and synthetic data, exhibit an improved generalization performance, achieving better precision rates when evaluated on real datasets that are visually distinct from the corresponding real training data.