Rotated Object Detector with Self- Attention and Improved IoU Loss

Nowadays in UAV and remote sensing image processing area, rotated object detection has been attached more and more attentions. However, there are few studies of this based on Transformer, which is much stronger than CNN for feature extraction. Moreover, the well-developed IoU based loss function in horizontal object detection area is not well fits with rotated objects. In this paper, we argue that Transformer of pyramid structure called Swin-Transformer is an effective alternative of CNN. Specifically, the finetuned Swin-Transformer with Relatively Position Encoding (RPE) performed much better than other backbones generally used. Moreover, the new kind of IoU loss called Gaussian Estimate Loss (GE Loss) that use gaussian kernel to model object is applied in our model. It can increase the precious of the model. This loss is in contract to other Gaussian modeling loss function for adding direction vector that can solve the difficulty of testing objects close to square. Experiments on DOTA dataset achieved 84.99% map, which indicates that and our experiment shows that these improvements of our model are effective.