<p>Oriented object detection in high-resolution remote sensing imagery is crucial for applications such as geographic information updating and maritime surveillance. This study introduces a Foreground-Guided Angle-Aware Feature Pyramid Network (FGAA-FPN) to address challenges posed by cluttered backgrounds, severe scale variations, and large orientation changes. The proposed FGAA-FPN adopts a hierarchy-aware design, where Foreground-Guided Feature Modulation calibrates low-level object responses before top-down propagation, while Angle-Aware Multi-Head Attention injects direction-biased interactions into high-level semantic features. Extensive experiments on DOTA v1.0 and DOTA v1.5 demonstrate that FGAA-FPN achieves competitive overall performance and leading neck-level results under comparable settings, reaching 75.5% and 68.3% mAP, respectively. Our findings highlight the potential of foreground-guided and angle-aware modeling in improving the accuracy and robustness of oriented object detection. The codes are available at <a href="https://github.com/sugmudy/FGAA-FPN">https://github.com/sugmudy/FGAA-FPN</a></p>