Theoretically Achieving Continuous Representation of Oriented Bounding Boxes

Considerable efforts have been devoted to Oriented Ob-ject Detection (OOD). However, one lasting issue regarding the discontinuity in Oriented Bounding Box (OBB) rep-resentation remains unresolved, which is an inherent bot-tleneck 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 di-rection. 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 Ambi-guity (DA) as discussed in literature. Specifically, we pro-pose a novel representation method called Continuous OBB (COBB), which can be readily integrated into existing de-tectors e.g. Faster-RCNN as a plugin. It can theoreti-cally 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 modu-larized benchmark based on the open-source deep learning framework Jittor's detection toolbox JDetfor OOD evaluation. On the popular DOTA dataset, by integrating Faster-RCNN as the same baseline model, our new method out-performs the peer method Gliding Vertex by 1.13% mAP<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> (relative improvement 1.54%), and 2.46% mAP<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">75</inf> (relative improvement 5.91%), without any tricks.