Due to the dense presence of multiple targets and complex background information in remote sensing images, existing detection algorithms often struggle to achieve satisfactory precision in small target detection. To address this issue, we propose a small target rotation box detection algorithm for remote sensing images called DRS-YOLO, based on reparameterized generalized pyramids and dilated residuals. First, to overcome the shortcomings of the backbone network in feature extraction, we enhance semantic information by incorporating a dilated residual module into the neck of the network, building upon the YOLOv8OBB framework. Second, to improve the network's performance in detecting multi-scale targets and facilitate the flow of low-level feature information to high-level features, we replace the neck structure with a reparameterized generalized feature pyramid network for more efficient multi-scale feature fusion, which aids in capturing high-level semantics and low-level spatial details. Finally, to further enhance the network's performance in detecting small targets, we propose the SPPFI module to expand the receptive field, thereby improving detection accuracy for remote sensing targets. Experimental results demonstrate that the improved algorithm achieves an increase in detection precision of 1.5% and 9.8% on the public DIOR and HRSC2016 datasets, respectively, compared to the original YOLOv8sOBB baseline network, indicating a significant enhancement in small target detection performance for remote sensing images.