Improving remote sensing object detection by using feature extraction and rotational equivariant attention

In recent years, computer vision has witnessed significant attention in the research on object detection in remote-sensing images. Unlike objects in traditional natural images, those in remote sensing images are captured vertically by spacecraft, introducing arbitrary directionality, substantial scale variation, and a more complex background. We propose a rotation-equivariant detector enhanced with feature fusion and attention modules to address remote sensing image object detection challenges. Specifically, we introduce a Rotation-Enhanced Feature Extraction (REFE) module and a Rotational Equivariant Attention (REA) module. These enhancements empower the detector to extract object information more effectively from remotely sensed images, filtering out complex background information and improving detection accuracy and stability. Through extensive experiments on diverse and challenging remote sensing image datasets, our method outperforms the task of object detection. Remarkably, our network achieves 1.0, 2.91, and 1.11 mean average precision (mAP) improvements on the DOTA-v1.5, HRSC2016 and DIOR-R datasets. These experimental results robustly demonstrate the effectiveness and superiority of our proposed method.