FLAD: Fusion-Based Long-Range Anti-UAV detection in complex backgrounds

Detecting small unmanned aerial vehicles (UAVs) in long-range visible-light imagery remains challenging because the targets occupy only a few pixels, have low signal-to-noise ratios, and are strongly affected by cluttered backgrounds and visually similar objects. To address these issues, we propose FLAD, a three-stage fusion-based detector built on a YOLO11-P2 baseline. The design targets a distinct failure mode at each stage of the detector: contour degradation in shallow features, clutter-induced ambiguity in intermediate representations, and localization instability in the detection head. First, LoGStem introduces Laplacian-of-Gaussian (LoG)-initialized edge enhancement together with Gaussian-initialized residual smoothing to strengthen contour cues and suppress background noise in shallow features. Second, RFAConvLSKBlock combines receptive-field attention convolution and large selective kernel (LSK) spatial attention to enhance contextual perception for tiny or weakly textured targets. Third, we adopt LSDC-Head, a lightweight decoupled detection head that uses shared detail-enhanced convolution (DEConv) blocks with group normalization and learnable per-level regression scaling, while retaining the YOLO-style distribution-based bounding-box regression formulation. Extensive experiments on three public benchmarks demonstrate consistent gains over the baseline: [email protected]:0.95 improves from 67.3 to 70.4% on DUT-Anti-UAV, from 28.0 to 31.4% on Drone-vs.-Bird, and from 29.8 to 35.1% on LRDD, with higher [email protected] across all three datasets. These results indicate that FLAD improves robustness for Anti-UAV detection under complex backgrounds and extreme scale variation while retaining real-time inference capability.