UAV-DETR：面向无人机影像的高效端到端目标检测
UAV-DETR: Efficient End-to-End Object Detection for Unmanned Aerial Vehicle Imagery
AI总结：
本文提出UAV-DETR，通过多尺度频率增强特征融合、频率聚焦下采样和语义对齐校准模块，提升无人机影像端到端目标检测精度与泛化能力。
摘要：
无人机目标检测（UAV-OD）已在多种场景中得到广泛应用。然而，现有大多数无人机目标检测算法依赖人工设计的组件，这些组件需要大量调参。不依赖此类人工设计组件的端到端模型主要面向自然图像设计，对无人机影像效果较差。为应对这些挑战，本文提出一种专为无人机影像定制的高效检测Transformer（DETR）框架，即UAV-DETR。该框架包含一个带频率增强模块的多尺度特征融合结构，能够在不同尺度上同时捕获空间信息与频率信息。此外，还提出了一个以频率为重点的下采样模块，用于在下采样过程中保留关键空间细节。同时开发了一个语义对齐与校准模块，用于对齐并融合来自不同融合路径的特征。实验结果表明，该方法在多个无人机影像数据集上具有有效性和良好的泛化能力。在VisDrone数据集上，与基线相比，本方法将AP提高了3.1%，将AP50提高了4.2%。在UAVVaste数据集上也观察到类似的性能提升。项目主页：https://github.com/ValiantDiligent/UAV-DETR
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
