Oriented object detection, an emerging task in recent years, aims to identify
and locate objects across varied orientations. This requires the detector to
accurately capture the orientation information, which varies significantly
within and across images. Despite the existing substantial efforts,
simultaneously ensuring model effectiveness and parameter efficiency remains
challenging in this scenario. In this paper, we propose a lightweight yet
effective \textbf{G}roup-wise \textbf{R}otating and \textbf{A}ttention (GRA)
module to replace the convolution operations in backbone networks for oriented
object detection. GRA can adaptively capture fine-grained features of objects
with diverse orientations, comprising two key components: Group-wise Rotating
and Group-wise Attention. Group-wise Rotating first divides the convolution
kernel into groups, where each group extracts different object features by
rotating at a specific angle according to the object orientation. Subsequently,
Group-wise Attention is employed to adaptively enhance the object-related
regions in the feature. The collaborative effort of these components enables
GRA to effectively capture the various orientation information while
maintaining parameter efficiency. Extensive experimental results demonstrate
the superiority of our method. For example, GRA achieves a new state-of-the-art
(SOTA) on the DOTA-v2.0 benchmark, while saving the parameters by nearly 50\%
compared to the previous SOTA method. Code will be released.

我们提出了一种轻量但有效的 G-R-A 模块，用于替代背景网络中的卷积操作，以实现有向目标检测，该模块可以自适应地捕捉具有不同旋转角度的目标特征，通过旋转卷积核和组内关注的协同作用，我们方法在 DOTA-v2.0 基准上实现了新的最先进水平，并节省了近 50％的参数。