Research Article

SynthSwarm: A controllable synthetic dataset for UAV Swarm detection

Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, PR China

^* Corresponding authors: Qing Yang, This email address is being protected from spambots. You need JavaScript enabled to view it. ; Xiwei Guo, This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 22 February 2026
Accepted: 7 May 2026

Abstract

Reliable detection of unmanned aerial vehicle (UAV) swarms is essential for airspace security and defense applications, however the scarcity of large-scale, densely annotated training data remains a critical bottleneck. Collecting real-world swarm data is costly, logistically challenging, and constrained by airspace regulations, while manual annotation of numerous small, fast-moving targets is time-consuming and prone to errors. 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, enabling 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 7000 high-resolution images (1920 × 1080) 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 one-stage detectors (YOLOX, YOLOv6, YOLOv12, YOLOv13), the two-stage detector Faster R-CNN, and the Transformer-based detector RT-DETR. Experimental results demonstrate that the dataset poses significant challenges for existing methods, particularly in high-density and small-target scenarios. Furthermore, cross-dataset on the MMFW-UAV 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.