UAV-DETR: DETR for Anti-Drone Target Detection
Jun Yang, Dong Wang, Hongxu Yin, Hongpeng Li, Jianxiong Yu

TL;DR
UAV-DETR is a real-time, efficient deep learning framework designed specifically for detecting tiny drones in complex environments, achieving superior accuracy and reduced computational cost.
Contribution
The paper introduces UAV-DETR, a novel architecture with specialized modules for small-target detection, combining high-frequency feature capture and multi-scale fusion, with a new loss strategy for improved accuracy.
Findings
Outperforms baseline RT-DETR with +6.61% mAP50:95
Reduces parameters by 39.8% compared to baseline
Achieves +1.4% precision and +1.0% F1-score on public benchmark
Abstract
Drone detection is pivotal in numerous security and counter-UAV applications. However, existing deep learning-based methods typically struggle to balance robust feature representation with computational efficiency. This challenge is particularly acute when detecting miniature drones against complex backgrounds under severe environmental interference. To address these issues, we introduce UAV-DETR, a novel framework that integrates a small-target-friendly architecture with real-time detection capabilities. Specifically, UAV-DETR features a WTConv-enhanced backbone and a Sliding Window Self-Attention (SWSA-IFI) encoder, capturing the high-frequency structural details of tiny targets while drastically reducing parameter overhead. Furthermore, we propose an Efficient Cross-Scale Feature Recalibration and Fusion Network (ECFRFN) to suppress background noise and aggregate multi-scale…
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Taxonomy
TopicsUAV Applications and Optimization · Advanced Neural Network Applications · Advanced SAR Imaging Techniques
