RBox-CNN
Jamyoung Koo, Junghoon Seo, Seunghyun Jeon, Jeongyeol Choe, Taegyun Jeon · 2018
In this paper, we propose a rotated bounding box based convolutional neural network (RBox-CNN) for arbitrary-oriented ship detection. RBox-CNN is an end-to-end model based on Faster R-CNN. The region proposal network generates proposals as the rotated bounding box, and then the rotation region-of-interest (RRoI) pooling layer is applied to extract region features corresponding the proposals. In addition, the diagonal region-of-interest (DRoI) pooling layer is applied simultaneously to extract context features and alleviate the problem of misalignment in RRoI pooling layer. To stably predict locations with the angle, we apply the regression of distance's projection in width/height. Experiments on HRSC2016 show that our model achieves state-of-the-art detection accuracy on ship detection. Furthermore, RBox-CNN achieves a significant improvement on DOTA for oriented general object detection in remote sensing images.