Center-Symmetry Representation-Based High-Quality Localization Detector for Oriented Object Detection

Abstract

Two-stage detectors are widely used in oriented object detection and have achieved high detection accuracy. However, some inconsistency issues in two-stage detectors limit their further improvement. To overcome these issues, this work proposes an effective two-stage center-symmetry representation-based high-quality localization detector (CR-HLDet) for oriented objects in remote sensing images. It improves the three main components of a typical two-stage detector: neck, region proposal network (RPN), and head. Specifically, in the neck, we introduce a feature enhancement block (FEB) to extract multilevel semantic information to solve the inconsistency in feature fusion. And we use a novel center-symmetry representation to generate high-quality oriented proposals in the RPN. Instead of the prediction of the rotated angle, it utilizes the symmetry of the center point to locate any oriented bounding box (OBB) by three points, which gets rid of the inconsistency between proposals and oriented objects. Finally, we design a dual classification detection head (DCDH) to obtain more robust and reasonable classification confidence. It solves the inconsistency between classification confidence and the regression quality by establishing the connection between the classification branch and the regression branch. The effective combination of the above three modules enables our CR-HLDet to accurately locate oriented objects in remote sensing images. Extensive and comprehensive experiments on three challenging remote sensing datasets (DOTA, HRSC2016, and DIOR-R) demonstrate that the proposed method achieves state-of-the-art performance.