UAV-SGDFNet: structured guided dual-level feature fusion for low-altitude anti-UAV detection
TL;DR
This work proposes UAV-SGDFNet, an efficient small UAV detector that enhances robustness by jointly optimizing feature representation and multi-scale fusion, and introduces a spatial-channel collaborative attention mechanism to strengthen discriminative features for the target and suppress background interference.
Abstract
In complex low-altitude environments, illegally operated UAVs are typically small and can easily be confused with background clutter, leading to reduced detection stability. To address this challenge, we propose UAV-SGDFNet, an efficient small UAV detector. The model enhances robustness by jointly optimizing feature representation and multi-scale fusion. In the feature extraction phase, we introduce a spatial-channel collaborative attention mechanism to strengthen discriminative features for the target and suppress background interference. In the feature fusion phase, we design a structurally guided dual-level fusion module that enables structured cross-level interaction through the cooperation of a guidance path and a fidelity path, mitigating semantic dilution and improving the localization of small targets. Experimental results on a complex-scene dataset show that the proposed method outperforms several mainstream detectors, achieving an mAP@0.5 of 0.748 and increasing Recall to 0.692, while maintaining high inference efficiency, indicating a favorable accuracy-efficiency trade-off. Ablation studies and visual analyses further confirm the effectiveness of the proposed components for stable small-target detection and indicate its potential for real-time deployment.