🤖 AI Summary
This work addresses the challenges of detecting rotated objects in high-resolution remote sensing imagery, where cluttered backgrounds, large scale variations, and complex orientations hinder performance. To tackle these issues, we propose a foreground-guided and angle-aware feature pyramid network that synergistically enhances object localization and orientation estimation. Specifically, foreground-guided feature modulation is introduced in low-level features to amplify responses in target regions, while an angle-aware multi-head attention mechanism is designed in high-level features to explicitly model directional geometric relationships. The model jointly optimizes foreground saliency and orientation priors under weak supervision. Our method achieves state-of-the-art results with mAP scores of 75.5% on DOTA v1.0 and 68.3% on DOTA v1.5, marking the first approach to successfully co-optimize foreground and angular information in a weakly supervised setting.
📝 Abstract
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.