Precise Vertex Regression and Feature Decoupling for Oriented Object Detection

Oriented object detection is a key task in the field of remote sensing image interpretation. Although extensive efforts have been made over the past few years, accurate oriented object detection remains a big challenge due to the dense arrangement and diverse orientations of objects. In this paper, we propose an oriented object detector based on the Faster R-CNN, which mainly consists of a Precise Vertex Regression (PVR) module and a Feature Decoupling (FD) module. Specifically, the PVR module predicts the arbitrary quadrilaterals of oriented objects with the precise vertex regression manner, which discretizes the regression range of vertex into several bins and applies a classification network to predict which bin the vertex belongs to. The FD module decouples the RoI features for classification and regression tasks by lightweight affine transformation. Experimental results on DOTA and DIOR-R datasets validate the effectiveness of our proposed method. Code is available at https://github.com/ShichengMiao16/VRDet.