EFE-Net: Edge-Aware Multi-Scale Feature Enhancement for Anti-UAV Detection

Anti-UAV detection has emerged as a critical task in airspace security, yet it remains challenging due to the small size, weak edge responses, and complex backgrounds of UAV targets. For small UAVs occupying only a few pixels, boundary pixels constitute the most informative geometric cues for localization. However, existing methods treat edge extraction as an implicit byproduct of feature learning, causing boundary details to degrade through successive layers without explicit preservation. To address this limitation, we propose EFE-Net, an edge-aware multi-scale feature enhancement network that establishes a unified edge centric pipeline where boundary information is explicitly extracted, selectively enhanced, and systematically propagated throughout the feature hierarchy. Specifically, we design a Frequency-enhanced Multi-Scale Edge Injector (FMSEI) that exploits the complementarity between spatial gradients and frequency-domain spectra to generate robust multi-scale edge representations at the finest pyramid scale. We then propose a Pyramid Contextual Edge Selection (PCES) module that derives selection weights from multi granularity contextual differences through differential operations, adaptively reinforcing boundary activations while suppressing re dundant responses in smooth regions. Furthermore, we introduce a Feature Aggregation Layer (FAL) equipped with the Dual-Domain Cross-Scale Aggregation (DDCSA) module, which applies learnable weights to selectively modulate low-frequency components while preserving high-frequency boundary structures; spatial attention is further incorporated to provide location-specific refinement. Extensive experiments on the DUT-Anti-UAV, Bird-UAV, and Anti UAV infrared (INF) datasets demonstrate that EFE-Net achieves superior performance compared to state-of-the-art methods. Cross domain evaluation on the VisDrone dataset further validates the generalization capability of our approach.