In this study, the integration of the rotation region proposal network with Faster R-CNN network along with an improved remote sensing image object detection method based on the convolutional neural network is proposed. The aim is two-fold: 1) to realize rapid and precise detection of remote sensing image objects; 2) to address the problem caused by objects with rotated angle. Compared to the mainstream target detection methods, the proposed method introduces the rotation factor to the region proposal network and generates proposal regions with different directions, aiming at the characteristics of variable direction and relative aggregation of most targets in the remote sensing image. The addition of a convolution layer before the fully connected layer of the Faster R-CNN network has the advantages of reducing the feature parameters, enhancing the performance of classifiers, and avoiding over-fitting. Compared with the state-of-the-art object detection methods, the proposed algorithm is able to combine the features extracted by the convolutional neural network in the rotation region proposal network with the multi-scale features. Therefore, significant improvement in remote sensing image object detection can be achieved.