Enhancing object detection for remote sensing with dynamic heads in Oriented R-CNN

This paper aims to enhance the Oriented R-CNN framework for object detection in aerial images. Leveraging a diverse collection of high-resolution aerial imagery, including buildings, vehicles, ships, and aircraft, we propose improvements to address challenges like object scale, orientation, and occlusion common in such images. While the Oriented R-CNN method has shown promise in handling oriented objects, challenges in scale and spatial awareness remain. We introduce a novel approach that enhances the Oriented R-CNN framework by integrating dynamic heads, combining scale and spatial awareness. Through scale-aware attention mechanisms, we address issues with object scale, and spatial-aware mechanisms capture fine-grained spatial information. Extensive experiments, including the Dota dataset, validate the method's superior performance. Our contribution enhances object detection precision in remote sensing, overcoming previous limitations.