Reliable detection of unmanned aerial vehicle (UAV) swarms is essential for airspace security and defense applications, yet the scarcity of large-scale, densely annotated training data remains a critical bottleneck. Collecting real-world swarm data is costly, logistically demanding, and constrained by airspace regulations, while manual annotation of numerous small, fast-moving targets is time-consuming and error-prone. To address these challenges, this paper presents SynthSwarm, a large-scale synthetic dataset specifically designed for UAV swarm detection in long-range aerial surveillance scenarios. The dataset is generated through a controllable simulation pipeline built on the Unity engine, which enables precise six-degree-of-freedom (6-DoF) pose specification for each UAV instance and automatic pixel-accurate bounding box annotation without manual labeling. SynthSwarm comprises 7, 000 high-resolution images (19201080) containing 31, 542 UAV instances, with systematic variations in swarm density, formation patterns, target scale, and environmental conditions. Statistical analysis reveals that 67. 3\% of the targets qualify as small objects, reflecting the inherent difficulty of detecting distant UAV swarms. We benchmark several representative deep learning detectors, including YOLOX, YOLOv13, YOLOv12, YOLOv6 on the proposed dataset. Experimental results demonstrate that the dataset poses significant challenges for existing methods, particularly in high-density and small-target scenarios. Furthermore, cross-dataset experiments validate the effectiveness of synthetic data as a pre-training source for improving detection performance on real UAV datasets. The dataset and generation pipeline are publicly available to facilitate further research in UAV swarm detection.
Yang et al. (2026) studied this question.