UFO-DETR: Frequency-Guided End-to-End Detector for UAV Tiny Objects

• URL: http://arxiv.org/abs/2602.22712v1
• Date: Thu, 26 Feb 2026 07:37:45 GMT
• Title: UFO-DETR: Frequency-Guided End-to-End Detector for UAV Tiny Objects
• Authors: Yuankai Chen, Kai Lin, Qihong Wu, Xinxuan Yang, Jiashuo Lai, Ruoen Chen, Haonan Shi, Minfan He, Meihua Wang,
• Abstract summary: Small target detection in UAV imagery faces significant challenges such as scale variations, dense distribution, and the dominance of small targets.<n>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.
• Score: 7.1136620172045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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