Abstract
Detecting small unmanned aerial vehicles (UAVs) from a ground-to-air (G2A) perspective presents significant challenges, including extremely low pixel occupancy, cluttered aerial backgrounds, and strict real-time constraints. Existing YOLO-based detectors are primarily optimized for general object detection and often lack adequate feature resolution for sub-pixel targets, while introducing complexities during deployment. In this paper, we propose SDD-YOLO, a small-target detection framework tailored for G2A anti-UAV surveillance. To capture fine-grained spatial details critical for micro-targets, SDD-YOLO introduces a P2 high-resolution detection head operating at 4 times downsampling. Furthermore, we integrate the recent architectural advancements from YOLO26, including a DFL-free, NMS-free architecture for streamlined inference, and the MuSGD hybrid training strategy with ProgLoss and STAL, which substantially mitigates gradient oscillation on sparse small-target signals. To support our evaluation, we construct DroneSOD-30K, a large-scale G2A dataset comprising approximately 30,000 annotated images covering diverse meteorological conditions. Experiments demonstrate that SDD-YOLO-n achieves a mAP@0.5 of 86.0% on DroneSOD-30K, surpassing the YOLOv5n baseline by 7.8 percentage points. Extensive inference analysis shows our model attains 226 FPS on an NVIDIA RTX 5090 and 35 FPS on an Intel Xeon CPU, demonstrating exceptional efficiency for future edge deployment.
Abstract (translated)
从地对空（G2A）视角检测小型无人机（UAV）面临显著挑战，包括极低的像素占比、杂乱的空中背景以及严格的实时性要求。现有基于YOLO的检测器主要针对通用目标检测进行优化，往往缺乏对亚像素目标的足够特征分辨率，同时在部署时引入复杂性。本文提出SDD-YOLO，一个专为G2A反无人机监控设计的小目标检测框架。为捕捉微目标所需的关键细粒度空间细节，SDD-YOLO引入了在4倍下采样率下运行的P2高分辨率检测头。此外，我们整合了YOLO26的最新架构进展，包括无DFL、无NMS的简化推理架构，以及结合渐进损失（ProgLoss）和空间时间自适应学习（STAL）的MuSGD混合训练策略，该策略显著缓解了稀疏小目标信号上的梯度振荡问题。为支持评估，我们构建了DroneSOD-30K数据集，这是一个包含约3万张标注图像的大规模G2A数据集，覆盖多样气象条件。实验表明，SDD-YOLO-n在DroneSOD-30K上达到86.0%的mAP@0.5，比YOLOv5n基线高出7.8个百分点。广泛的推理分析显示，我们的模型在NVIDIA RTX 5090上达到226 FPS，在Intel Xeon CPU上达到35 FPS，展现了面向未来边缘部署的卓越效率。
URL
https://arxiv.org/abs/2603.25218
