Oriented object detection is one of the most fundamental and challenging tasks in remote sensing, aiming to locate and classify objects with arbitrary orientations. Recent advancements in deep learning have significantly enhanced the capabilities of oriented object detection. Given the rapid development of this field, this paper presents a comprehensive survey of recent advances in oriented object detection. To be specific, we begin by tracing the technical evolution from horizontal object detection to oriented object detection and highlighting the specific challenges, including feature misalignment, spatial misalignment, and oriented bounding box (OBB) regression problems. Subsequently, we further categorize existing methods into detection framework, OBB regression, and feature representations, and provide an in-depth discussion on how these approaches address the above challenges. In addition, we cover several publicly available datasets and evaluation protocols. Furthermore, we provide a comprehensive comparison and analysis of state-of-the-art methods. Toward the end of this paper, we identify several future directions for oriented object detection.

翻译：定向目标检测是遥感领域中最基础且最具挑战性的任务之一，其目标是对任意方向的物体进行定位与分类。深度学习的最新进展显著提升了定向目标检测的性能。鉴于该领域的快速发展，本文对定向目标检测的最新进展进行了全面综述。具体而言，我们首先追溯从水平目标检测到定向目标检测的技术演进历程，并重点阐述了其面临的特定挑战，包括特征错位、空间错位以及定向边界框回归问题。随后，我们将现有方法进一步归类为检测框架、OBB回归和特征表示三大类别，并深入探讨了这些方法如何应对上述挑战。此外，我们介绍了多个公开可用的数据集与评估标准。进一步地，我们对当前最优方法进行了综合比较与分析。在本文结尾，我们指出了定向目标检测未来发展的若干方向。