Theoretically Achieving Continuous Representation of Oriented Bounding Boxes
arxiv(2024)
摘要
Considerable efforts have been devoted to Oriented Object Detection (OOD).
However, one lasting issue regarding the discontinuity in Oriented Bounding Box
(OBB) representation remains unresolved, which is an inherent bottleneck for
extant OOD methods. This paper endeavors to completely solve this issue in a
theoretically guaranteed manner and puts an end to the ad-hoc efforts in this
direction. Prior studies typically can only address one of the two cases of
discontinuity: rotation and aspect ratio, and often inadvertently introduce
decoding discontinuity, e.g. Decoding Incompleteness (DI) and Decoding
Ambiguity (DA) as discussed in literature. Specifically, we propose a novel
representation method called Continuous OBB (COBB), which can be readily
integrated into existing detectors e.g. Faster-RCNN as a plugin. It can
theoretically ensure continuity in bounding box regression which to our best
knowledge, has not been achieved in literature for rectangle-based object
representation. For fairness and transparency of experiments, we have developed
a modularized benchmark based on the open-source deep learning framework
Jittor's detection toolbox JDet for OOD evaluation. On the popular DOTA
dataset, by integrating Faster-RCNN as the same baseline model, our new method
outperforms the peer method Gliding Vertex by 1.13
1.54
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要