Arbitrary-oriented object representations contain the oriented bounding box (OBB), quadrilateral bounding box (QBB), and point set (PointSet). Each representation encounters problems that correspond to its characteristics, such as the boundary discontinuity, square-like problem, representation ambiguity, and isolated points, which lead to inaccurate detection. Although many effective strategies have been proposed for various representations, there is still no unified solution. Current detection methods based on Gaussian modeling have demonstrated the possibility of breaking this dilemma; however, they remain limited to OBB. To go further, in this paper, we propose a unified Gaussian representation called G-Rep to construct Gaussian distributions for OBB, QBB, and PointSet, which achieves a unified solution to various representations and problems. Specifically, PointSet or QBB-based objects are converted into Gaussian distributions, and their parameters are optimized using the maximum likelihood estimation algorithm. Then, three optional Gaussian metrics are explored to optimize the regression loss of the detector because of their excellent parameter optimization mechanisms. Furthermore, we also use Gaussian metrics for sampling to align label assignment and regression loss. Experimental results on several public available datasets, DOTA, HRSC2016, UCAS-AOD, and ICDAR2015 show the excellent performance of the proposed method for arbitrary-oriented object detection. The code has been open sourced at https://github.com/open-mmlab/mmrotate.

翻译：任意导向的物体表示方式包含定向约束框(OBB)、四边约束框(QBB)和点集(PointSet),每个表示方式都遇到与其特点相符的问题,如边界不连续、平方问题、代表模糊和孤立点,导致检测不准确。虽然为各种表示方式提出了许多有效战略,但仍然没有统一的解决办法。目前基于高斯模型的检测方法表明有可能打破这一困境;然而,这些方法仍然局限于OBB。为了更进一步,我们建议采用一个统一的高斯代表方式(G-Rep)来为OBB、QBB和PointSet建立高斯分布,从而实现各种表示和问题的统一解决方案。具体地说,点Set或基于QBB的物体被转换成高斯分布式,并且使用最大可能性估算算法优化了它们的参数。然后,探索了三个开放式的衡量标准,以优化探测器的回归损失,因为其参数最优的优化机制。此外,我们还利用Gabs-Repas-AR 用于SAR 的SBA-Regalalalalalalalalalalal IMal-Iard IMLAGLA 。