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 semantics. To further enhance accuracy, UAV-DETR incorporates a hybrid Inner-CIoU and NWD loss strategy, mitigating the extreme sensitivity of standard IoU metrics to minor positional deviations in small objects. Extensive experiments demonstrate that UAV-DETR significantly outperforms the baseline RT-DETR on our custom UAV dataset (+6.61% in mAP50:95, with a 39.8% reduction in parameters) and the public DUT-ANTI-UAV benchmark (+1.4% in Precision, +1.0% in F1-Score). These results establish UAV-DETR as a superior trade-off between efficiency and precision in counter-UAV object detection. The code is available at this https URL.
Abstract (translated)
无人机检测在众多安全与反无人机应用中至关重要。然而，现有的基于深度学习的方法通常难以在鲁棒的特征表示与计算效率之间取得平衡。这一挑战在复杂背景与严重环境干扰下检测微型无人机时尤为突出。为应对这些问题，我们提出了UAV-DETR，一种集成了小目标友好架构与实时检测能力的新型框架。具体而言，UAV-DETR采用WTConv增强的骨干网络与滑动窗口自注意力（SWSA-IFI）编码器，在捕获微小目标高频结构细节的同时大幅减少参数开销。此外，我们提出了高效跨尺度特征重校准与融合网络（ECFRFN）以抑制背景噪声并聚合多尺度语义信息。为进一步提升精度，UAV-DETR结合了混合Inner-CIoU与NWD损失策略，缓解了标准IoU指标对小目标微小位置偏差的极端敏感性。大量实验表明，UAV-DETR在我们自建的无人机数据集上显著优于基线模型RT-DETR（mAP50:95提升6.61%，参数量减少39.8%），并在公开的DUT-ANTI-UAV基准上实现精度提升1.4%、F1分数提升1.0%。这些结果确立了UAV-DETR在反无人机目标检测中效率与精度之间的优异权衡。代码可通过此https链接获取。
URL
https://arxiv.org/abs/2603.22841
