Detecting the arbitrary-oriented objects in aerial images is a significant yet challenging task in remote sensing image analysis. Existing methods generally use the oriented bounding boxes (OBBs) as Region of Interests (RoIs) to detect aerial objects, and learn the orientation angle information of objects under the supervision of OBB annotations. However, directly learning the orientation angle suffers from the training instability due to the existence of angular periodicity issue in object orientation prediction. In this study, we propose a novel axis projection-based angle learning network (termed as AProNet) for robust oriented object detection in aerial images. Instead of using the direct angle representation, we design an axis projection-based angle representation that is achieved by projecting the long axis of an aerial object along the X- and Y-axes in the image coordinate system. In this way, AProNet can obtain the orientation angle of objects based on the predicted axis projections, which are free of angular periodicity issue. Accordingly, a new loss function is developed to guide the training of AProNet. The loss function measures the loss between the predicted and groudtruth axis projections of objects and also dynamically balances the learning of the different OBB parameters. We also introduce a feature enhancement module to enhance the multi-scale features extracted by AProNet with geometric clues that are highly related to the axis projection-based angle learning. Extensive experiments demonstrate that the proposed axis projection-based angle learning can effectively handle the angular periodicity issue, achieving a competitive performance on two commonly-used aerial datasets DOTA and HRSC2016.

检测航拍图像中任意方向的物体是遥感图像分析中一项重要但具有挑战性的任务。现有方法通常使用定向边界框（OBB）作为兴趣区域（RoI）来检测空中物体，并在 OBB 注释的监督下学习物体的方向角信息。然而，由于目标方向预测中存在角度周期性问题，直接学习方向角会受到训练不稳定的影响。在这项研究中，我们提出了一种新颖的基于轴投影的角度学习网络（称为 AProNet），用于航拍图像中的鲁棒定向目标检测。而不是使用直角表示，我们设计了一种基于轴投影的角度表示，通过在图像坐标系中沿 X 轴和 Y 轴投影空中物体的长轴来实现。通过这种方式，AProNet 可以根据预测的轴投影获得物体的方向角，没有角度周期性问题。因此，开发了一个新的损失函数来指导 AProNet 的训练。损失函数测量对象的预测和真实轴投影之间的损失，并动态平衡不同 OBB 参数的学习。我们还引入了一个特征增强模块，通过与基于轴投影的角度学习高度相关的几何线索来增强 AProNet 提取的多尺度特征。