SRCF-UAV: Sparse Radar-Camera Fusion for 3D UAV Detection

Abstract

With the rapid development of the low-altitude economy, accurate detection and localization of UAVs have become increasingly important. Conventional radar and visual detection methods have low accuracy, whereas current radar-camera fusion methods are computationally intensive. To overcome these issues, we propose a novel 3D UAV detection approach based on sparse radar-camera fusion, called SRCF-UAV, to achieve high-precision, low-complexity UAV detection in diverse scenarios. Specifically, we first propose an improved query initialization method that incorporates locations from 2D image proposals and radar point clouds. Then, we propose a query update method that sparsely fuses radar and image queries based on features, velocity, and spatial distance. Furthermore, we develop a radar-camera multimodal data collection platform based on real-time kinematic positioning (RTK) and collect a dataset of centimeter-level precision, comprising over 20,000 UAV instances that cover various scenarios, UAV models, and lighting conditions. Finally, extensive experiments on this dataset demonstrate that the proposed approach can achieve an average precision of up to 91.65% and an inference latency as low as 17 ms, validating its effectiveness and efficiency. The dataset and code will be publicly available to support further research.