Small target detection in UAV imagery faces significant challenges such as scale variations, dense distribution, and the dominance of small targets. Existing algorithms rely on manually designed components, and general-purpose detectors are not optimized for UAV images, making it difficult to balance accuracy and complexity. To address these challenges, this paper proposes an end-to-end object detection framework, UFO-DETR, which integrates an LSKNet-based backbone network to optimize the receptive field and reduce the number of parameters. By combining the DAttention and AIFI modules, the model flexibly models multi-scale spatial relationships, improving multi-scale target detection performance. Additionally, the DynFreq-C3 module is proposed to enhance small target detection capability through cross-space frequency feature enhancement. Experimental results show that, compared to RT-DETR-L, the proposed method offers significant advantages in both detection performance and computational efficiency, providing an efficient solution for UAV edge computing.
@article{arxiv.2602.22712,
title = {UFO-DETR: Frequency-Guided End-to-End Detector for UAV Tiny Objects},
author = {Yuankai Chen and Kai Lin and Qihong Wu and Xinxuan Yang and Jiashuo Lai and Ruoen Chen and Haonan Shi and Minfan He and Meihua Wang},
journal= {arXiv preprint arXiv:2602.22712},
year = {2026}
}
Comments
6 pages, 6 figures, published to 2026 International Conference on Computer Supported Cooperative Work in Design