Abstract: In order to improve the detection accuracy of the ship detection in optical remote sensing images and the complexity of the detection model, the R²CNN（Rotational Region Convolutional Neural Networks） is introduced to develop a two-stage ship detection model with rotated anchor box. In the first stage, the detection algorithm generates horizontal boundary boxes to represent the areas covering target candidates. The prediction branches for processing the horizontal boundary boxes and a regression model for indirectly predicting target orientation are introduced for the second stage operation of the model. An IoU（Intersection over Union） algorithm which uses mask matrix for non-maximum suppression processing of rotated boundary boxes is developed and integrated in the model. The model is verified with dataset HRSC 2016 and an average precision of 81.0% is achieved.