Due to the frequent variability of object orientation, accurate prediction of orientation information remains a challenge in oriented object detection. To better extract orientation-related information, current methods primarily focus on the design of reasonable representations of oriented bounding box (OBB) and rotation-sensitive feature extraction. However, existing OBB representations often suffer from boundary discontinuity and representation ambiguity problems. Methods of designing continuous and unambiguous regression losses do not essentially solve such problems. Gaussian bounding box (GBB) avoids these OBB representation problems, but directly regressing GBB is susceptible to numerical instability. In this paper, we propose linear GBB (LGBB), a novel OBB representation. By linearly transforming the elements of GBB, LGBB does not have the boundary discontinuity and representation ambiguity problems, and have high numerical stability. On the other hand, current rotation-sensitive feature extraction methods based on convolutions can only extract features under a local receptive field, which is slow in aggregating rotation-sensitive features. To address this issue, we propose ring-shaped rotated convolution (RRC). By adaptively rotating feature maps to arbitrary orientations, RRC extracts rotation-sensitive features under a ring-shaped receptive field, rapidly aggregating rotation-sensitive features and contextual information. RRC can be applied to various models in a plug-and-play manner. Experimental results demonstrate that the proposed LGBB and RRC are effective and achieve state-of-the-art (SOTA) performance. By integrating LGBB and RRC into various models, the detection accuracy is effectively improved on DOTA and HRSC2016 datasets.

翻译：由于目标方向的频繁变化，准确预测方向信息仍是旋转目标检测中的挑战。为更好地提取方向相关特征，现有方法主要聚焦于合理的旋转边界框（OBB）表示设计和旋转敏感特征提取。然而，现有OBB表示常存在边界不连续和表示歧义问题，设计连续且无歧义的回归损失函数并不能从本质上解决这些问题。高斯边界框（GBB）避免了OBB的表示问题，但直接回归GBB易受数值不稳定性影响。本文提出线性GBB（LGBB）这一新型OBB表示方法。通过对GBB元素进行线性变换，LGBB既无边界不连续和表示歧义问题，又具有高数值稳定性。另一方面，现有基于卷积的旋转敏感特征提取方法仅能在局部感受野下提取特征，导致旋转敏感特征聚合速度缓慢。针对此问题，我们提出环形旋转卷积（RRC）。通过自适应地将特征图旋转至任意方向，RRC在环形感受野下提取旋转敏感特征，快速聚合旋转敏感特征与上下文信息。RRC可即插即用应用于各类模型。实验结果表明，所提LGBB与RRC方法有效且达到最优（SOTA）性能。将LGBB与RRC集成至不同模型，在DOTA和HRSC2016数据集上均有效提升了检测精度。