[2603.22841] UAV-DETR: DETR for Anti-Drone Target Detection

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.22841 (cs) [Submitted on 24 Mar 2026] Title:UAV-DETR: DETR for Anti-Drone Target Detection Authors:Jun Yang, Dong Wang, Hongxu Yin, Hongpeng Li, Jianxiong Yu View a PDF of the paper titled UAV-DETR: DETR for Anti-Drone Target Detection, by Jun Yang and Dong Wang and Hongxu Yin and Hongpeng Li and Jianxiong Yu View PDF HTML (experimental) 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 dev...