Abstract: The detection and recognition of ships in high-resolution remote sensing images play a vital role in the understanding of remote sensing images. Due to the characteristics of ships in high-resolution remote sensing images(such as viewing angle of images, distribution of objects, various scale of targets, etc.), simply applying the detection algorithm for natural images to remote sensing images can hardly obtain satisfactory performance. To this end, this paper proposes a ship object detection algorithm in remote sensing based with Rotated Rectangular Region. Firstly, a rotation region representation method is introduced to locate and classify the ship objects precisely. Secondly, pyramid pooling module of region of interest(RoI) is proposed, which integrates the multi-scale pooling features of RoI to adapt to the large scale range of the ship target. Finally, localization confidence prediction branch is designed to use intersection over union(IoU) guided non-maximum suppression, which optimizes the post-processing results. Experiments on HRSC2016 dataset show that our method outperforms exiting methods.