Performance comparison on DOTA-v2.0 dataset.

Oriented small object detection remains a challenging problem in computer vision, largely due to the weak feature representation and high computational cost of existing detection Transformer (DETR)-based detectors. To address these issues, this work presents Frequency Domain Awareness Detection Transformer (FDA-DETR), an end-to-end framework that enhances both accuracy and efficiency for oriented small object detection. The core of FDA-DETR lies in its multi-scale frequency domain enhancement, which amplifies high-frequency details crucial for small object discrimination. And by introducing a density-aware dynamic query mechanism, the model further adapts computational resource allocation to object density and orientation, improving detection in complex scenes. To balance global context and local detail, a multi-granularity attention fusion module is incorporated, while an adaptive multi-task loss based on Bayesian uncertainty enables dynamic optimization across multiple objectives. Experiments on public datasets show that FDA-DETR achieves higher detection accuracy and faster inference speed compared to existing DETR-based methods, particularly for small and densely distributed objects. These results, supported by theoretical analysis and ablation studies, highlight the effectiveness and synergy of the proposed modules. FDA-DETR thus provides a robust solution for oriented small object detection and offers new perspectives for future research on feature learning and attention mechanisms.定向小目标检测（Oriented small object detection）仍是计算机视觉领域极具挑战性的研究课题，究其核心原因，现有基于检测Transformer（DETR）的检测器普遍存在特征表征能力不足、计算开销过大的问题。为解决上述痛点，本文提出频域感知检测Transformer（FDA-DETR）——一款可同时提升定向小目标检测精度与效率的端到端（end-to-end）框架。FDA-DETR的核心在于多尺度频域增强模块，该模块可放大对小目标判别至关重要的高频细节信息；此外，通过引入密度感知动态查询机制（density-aware dynamic query mechanism），模型可根据目标密度与方位动态适配计算资源分配，进而优化复杂场景下的检测效果。为平衡全局上下文与局部细节，本文集成了多粒度注意力融合模块（multi-granularity attention fusion module）；同时，基于贝叶斯不确定性（Bayesian uncertainty）的自适应多任务损失函数可实现多目标动态优化。在公开数据集上开展的实验结果表明，相较于现有基于DETR的检测方法，FDA-DETR可获得更高的检测精度与更快的推理速度，尤其在小目标与密集分布目标场景中表现突出。结合理论分析与消融实验（ablation studies）的验证结果，进一步凸显了本文所提各模块的有效性与协同性。综上，FDA-DETR为定向小目标检测提供了鲁棒可靠的解决方案，同时为未来特征学习与注意力机制相关研究提供了全新视角。