针对无人机图像设计高效端到端目标检测框架，提升小目标识别效果。
UAV-DETR: Efficient End-to-End Object Detection for Unmanned Aerial Vehicle Imagery
- 引入多尺度频域增强特征融合模块，捕捉不同层级的空间与频率信息。
- 在下采样时保留关键空间细节，提升小目标检测精度，VisDrone上AP提升3.1%。
- 适用于多种无人机图像数据集，尤其适合小目标密集场景的检测任务。
无人机目标检测（UAV-OD）已广泛应用于各类场景。然而，现有大多数算法依赖人工设计组件，需大量调参。面向自然图像的端到端模型在无人机影像上表现不佳。为此，本文提出专为无人机影像优化的高效检测变换器框架——UAV-DETR。该框架包含多尺度特征融合与频域增强模块，可同时捕获不同尺度的空间与频率信息；提出频域聚焦下采样模块，有效保留下采样过程中的关键空间细节；设计语义对齐与校准模块，实现多路径特征的精准融合。实验结果表明，本方法在多个无人机图像数据集上具有优异性能与泛化能力。在VisDrone数据集上，相比基线模型AP提升3.1%，$ ext{AP}_{50}$提升4.2%；UAVVaste数据集上也获得类似增益。
原文摘要 · Abstract (English)
Unmanned aerial vehicle object detection (UAV-OD) has been widely used in various scenarios. However, most existing UAV-OD algorithms rely on manually designed components, which require extensive tuning. End-to-end models that do not depend on such manually designed components are mainly designed for natural images, which are less effective for UAV imagery. To address such challenges, this paper proposes an efficient detection transformer (DETR) framework tailored for UAV imagery, i.e., UAV-DETR. The framework includes a multi-scale feature fusion with frequency enhancement module, which captures both spatial and frequency information at different scales. In addition, a frequency-focused down-sampling module is presented to retain critical spatial details during down-sampling. A semantic alignment and calibration module is developed to align and fuse features from different fusion paths. Experimental results demonstrate the effectiveness and generalization of our approach across various UAV imagery datasets. On the VisDrone dataset, our method improves AP by 3.1\% and $\text{AP}_{50}$ by 4.2\% over the baseline. Similar enhancements are observed on the UAVVaste dataset. The project page: https://github.com/ValiantDiligent/UAV-DETR
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