Refined Single-Stage Detector with Deformable Multi-Scale Feature Refinement for Remote Sensing Images

Remote sensing images often contain objects with diverse orientations, making rotated object detectors particularly suitable for remote sensing object detection. Refined single-stage detectors, comprising two single-stage detectors and a feature alignment module, have achieved significant advancements. However, aligning features of large-scale rotated objects against complex backgrounds in remote sensing images remains challenging. To overcome this, we have enhanced the feature alignment module and proposed the Deformable Multi-scale Refined Rotation RetinaNet (DMR3Det). It begins by identifying rotational key points of objects against background and then employs an attention mechanism to integrate these pivotal features for refined detection, achieving global multi-scale feature extraction of rotated objects in remote sensing images. Experiments on DOTA1.0 demonstrate that DMR3Det outperforms recent single-stage detectors like Oriented RepPoints by over 1.7% in mean Average Precision (mAP).