Oriented object detection is one of the most fundamental and challenging tasks in remote sensing, aiming at locating the oriented objects of numerous predefined object categories. Recently, deep learning based methods have achieved remarkable performance in detecting oriented objects in optical remote sensing imagery. However, a thorough review of the literature in remote sensing has not yet emerged. Therefore, we give a comprehensive survey of recent advances and cover many aspects of oriented object detection, including problem definition, commonly used datasets, evaluation protocols, detection frameworks, oriented object representations, and feature representations. Besides, the state-of-the-art methods are analyzed and discussed. We finally discuss future research directions to put forward some useful research guidance. We believe that this survey shall be valuable to researchers across academia and industry

翻译：有向目标检测是遥感领域中最为基础且富有挑战性的任务之一，旨在定位大量预定义目标类别中的有向目标。近年来，基于深度学习的方法在光学遥感图像中的有向目标检测方面取得了显著性能。然而，遥感领域尚未出现全面的文献综述。因此，本文对近期进展进行了全面综述，涵盖有向目标检测的多个方面，包括问题定义、常用数据集、评估协议、检测框架、有向目标表示以及特征表示。此外，本文分析并讨论了最先进的方法，最后探讨了未来研究方向，以提供一些有益的研究指导。我们相信，本综述对学术界和工业界的研究人员均具有重要价值。