With the increasing demand for oriented object detection e.g. in autonomous driving and remote sensing, the oriented annotation has become a labor-intensive work. To make full use of existing horizontally annotated datasets and reduce the annotation cost, a weakly-supervised detector H2RBox for learning the rotated box (RBox) from the horizontal box (HBox) has been proposed and received great attention. This paper presents a new version, H2RBox-v2, to further bridge the gap between HBox-supervised and RBox-supervised oriented object detection. While exploiting axisymmetry via flipping and rotating consistencies is available through our theoretical analysis, H2RBox-v2, using a weakly-supervised branch similar to H2RBox, is embedded with a novel self-supervised branch that learns orientations from the symmetry inherent in the image of objects. Complemented by modules to cope with peripheral issues, e.g. angular periodicity, a stable and effective solution is achieved. To our knowledge, H2RBox-v2 is the first symmetry-supervised paradigm for oriented object detection. Compared to H2RBox, our method is less susceptible to low annotation quality and insufficient training data, which in such cases is expected to give a competitive performance much closer to fully-supervised oriented object detectors. Specifically, the performance comparison between H2RBox-v2 and Rotated FCOS on DOTA-v1.0/1.5/2.0 is 72.31%/64.76%/50.33% vs. 72.44%/64.53%/51.77%, 89.66% vs. 88.99% on HRSC, and 42.27% vs. 41.25% on FAIR1M.

翻译：随着自动驾驶和遥感等领域对旋转目标检测需求的日益增长，旋转标注已成为一项劳动密集型工作。为充分利用现有水平标注数据集并降低标注成本，一种用于从水平框（HBox）学习旋转框（RBox）的弱监督检测器H2RBox已被提出并受到广泛关注。本文提出新版本H2RBox-v2，旨在进一步缩小HBox监督与RBox监督的旋转目标检测之间的差距。通过理论分析，我们利用翻转和旋转一致性来挖掘轴对称性，H2RBox-v2在采用类似H2RBox的弱监督分支的同时，嵌入了一个新颖的自监督分支，该分支从图像中物体固有的对称性学习方向。结合应对角度周期性等附属问题的模块，我们实现了一种稳定有效的解决方案。据我们所知，H2RBox-v2是旋转目标检测领域首个对称监督范式。与H2RBox相比，我们的方法不易受低标注质量和不充分训练数据的影响，在此类情况下有望展现出更接近全监督旋转目标检测器的竞争力。具体而言，在DOTA-v1.0/1.5/2.0上，H2RBox-v2与Rotated FCOS的性能对比为72.31%/64.76%/50.33% vs. 72.44%/64.53%/51.77%，在HRSC上为89.66% vs. 88.99%，在FAIR1M上为42.27% vs. 41.25%。