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

TL;DR

UFO-DETR is an end-to-end UAV object detector that enhances small target detection by integrating frequency guidance, multi-scale spatial modeling, and an optimized backbone, achieving improved accuracy and efficiency.

Contribution

This paper introduces UFO-DETR, a novel frequency-guided detection framework with a specialized backbone and modules for better small object detection in UAV imagery.

Findings

Outperforms RT-DETR-L in detection accuracy

Reduces model complexity and computational cost

Enhances small target detection through frequency feature enhancement

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.