With the increasing availability of high-resolution remote sensing and aerial imagery, oriented object detection has become a key capability for geographic information updating, maritime surveillance, and disaster response. However, it remains challenging due to cluttered backgrounds, severe scale variation, and large orientation changes. Existing approaches largely improve performance through multi-scale feature fusion with feature pyramid networks or contextual modeling with attention, but they often lack explicit foreground modeling and do not leverage geometric orientation priors, which limits feature discriminability. To overcome these limitations, we propose FGAA-FPN, a Foreground-Guided Angle-Aware Feature Pyramid Network for oriented object detection. FGAA-FPN is built on a hierarchical functional decomposition that accounts for the distinct spatial resolution and semantic abstraction across pyramid levels, thereby strengthening multi-scale representations. Concretely, a Foreground-Guided Feature Modulation module learns foreground saliency under weak supervision to enhance object regions and suppress background interference in low-level features. In parallel, an Angle-Aware Multi-Head Attention module encodes relative orientation relationships to guide global interactions among high-level semantic features. Extensive experiments on DOTA v1.0 and DOTA v1.5 demonstrate that FGAA-FPN achieves state-of-the-art results, reaching 75.5% and 68.3% mAP, respectively.

翻译：随着高分辨率遥感与航空影像的日益普及，旋转目标检测已成为地理信息更新、海洋监测与灾害响应的关键能力。然而，由于背景杂乱、尺度变化剧烈以及方向变化大，该任务仍面临挑战。现有方法主要通过特征金字塔网络进行多尺度特征融合，或利用注意力机制进行上下文建模以提升性能，但往往缺乏显式的前景建模，且未充分利用几何方向先验，这限制了特征的判别能力。为克服这些局限，我们提出FGAA-FPN——一种用于旋转目标检测的前景引导角度感知特征金字塔网络。FGAA-FPN基于分层功能分解构建，该分解考虑了金字塔各层级间不同的空间分辨率与语义抽象程度，从而增强了多尺度表征能力。具体而言，前景引导特征调制模块在弱监督下学习前景显著性，以增强低层特征中的目标区域并抑制背景干扰；同时，角度感知多头注意力模块编码相对方向关系，以引导高层语义特征间的全局交互。在DOTA v1.0和DOTA v1.5数据集上的大量实验表明，FGAA-FPN取得了最先进的性能，分别达到了75.5%和68.3%的mAP。