Rethinking Anchor-Object Matching and Encoding in Rotating Object Detection

Abstract

Rotating object detection is more challenging than horizontal object detection because of the multi-orientation of the objects involved. In the recent anchor-based rotating object detector, the IoU-based matching mechanism has some mismatching and wrong-matching problems. Moreover, the encoding mechanism does not correctly reflect the location relationships between anchors and objects. In this paper, RBox-Diff-based matching (RDM) mechanism and angle-first encoding (AE) method are proposed to solve these problems. RDM optimizes the anchor-object matching by replacing IoU (Intersection-over-Union) with a new concept called RBox-Diff, while AE optimizes the encoding mechanism to make the encoding results consistent with the relative position between objects and anchors more. The proposed methods can be easily applied to most of the anchor-based rotating object detectors without introducing extra parameters. The extensive experiments on DOTA-v1.0 dataset show the effectiveness of the proposed methods over other advanced methods.