针对无人机小目标检测难题，提出高效端到端检测框架UFO-DETR。
UFO-DETR: Frequency-Guided End-to-End Detector for UAV Tiny Objects
- 引入基于LSKNet的骨干网络与DAttention+AIFI模块，灵活建模多尺度空间关系。
- 在VisDrone2019数据集上比RT-DETR-L提升3.2% AP，参数量减少28%。
- 适合边缘计算场景，特别适用于无人机平台的小目标实时检测。
无人机影像中的小目标检测面临尺度变化大、密集分布以及小目标主导等挑战。现有算法依赖人工设计组件，通用检测器未针对无人机图像优化，难以兼顾精度与复杂度。本文提出端到端目标检测框架UFO-DETR，采用基于LSKNet的骨干网络以优化感受野并减少参数量；结合DAttention与AIFI模块，灵活建模多尺度空间关系，提升多尺度目标检测性能；提出DynFreq-C3模块，通过跨空间频率特征增强强化小目标检测能力。实验表明，相较于RT-DETR-L，该方法在检测性能与计算效率上均有显著优势，在VisDrone2019数据集上实现3.2%的AP提升，同时参数量降低28%，为无人机边缘计算提供高效解决方案。
原文摘要 · Abstract (English)
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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