OS-FPI: A Coarse-to-Fine One-Stream Network for UAV Geolocalization

Abstract

The geolocalization and navigation technology of unmanned aerial vehicles (UAVs) in denied environments is currently a prominent research area. Prior approaches mainly employed a two-stream network with nonshared weights to extract features from UAV and satellite images separately, followed by related modeling to obtain the response map. However, the two-stream network extracts UAV and satellite features independently. This approach significantly affects the efficiency of feature extraction and increases the computational load. To address these issues, we propose a novel coarse-to-fine one-stream network. Our approach allows information exchange between UAV and satellite features during early image feature extraction. To improve the model's performance, the framework retains feature maps generated at different stages of the feature extraction process for the feature fusion network and establishes additional connections between UAV and satellite feature maps in the feature fusion network. In addition, the framework introduces offset prediction to further refine and optimize the model's prediction results based on the classification tasks. Our proposed model boasts a similar inference speed to FPI while significantly reducing the number of parameters. It can achieve better performance with fewer parameters under the same conditions. Moreover, it achieves state-of-the-art performance on the UL14 dataset. Compared with previous models, our model achieved a significant 10.92-point improvement on the RDS metric, reaching 76.25. Furthermore, its performance in meter-level localization accuracy is impressive, with 82.62% improvement in 3-m accuracy, 64.17% improvement in 5-m accuracy, and 37.43% improvement in 10-m accuracy.

Keywords

• Deep learning
• geolocalization
• satellite
• unmanned aerial vehicle (UAV)