Remote sensing object detection (RSOD), one of the most fundamental and challenging tasks in the remote sensing field, has received longstanding attention. In recent years, deep learning techniques have demonstrated robust feature representation capabilities and led to a big leap in the development of RSOD techniques. In this era of rapid technical evolution, this review aims to present a comprehensive review of the recent achievements in deep learning based RSOD methods. More than 300 papers are covered in this review. We identify five main challenges in RSOD, including multi-scale object detection, rotated object detection, weak object detection, tiny object detection, and object detection with limited supervision, and systematically review the corresponding methods developed in a hierarchical division manner. We also review the widely used benchmark datasets and evaluation metrics within the field of RSOD, as well as the application scenarios for RSOD. Future research directions are provided for further promoting the research in RSOD.

翻译：遥感目标检测（RSOD）是遥感领域中最基础且最具挑战性的任务之一，长期以来一直受到广泛关注。近年来，深度学习技术展现出强大的特征表示能力，极大地推动了RSOD技术的发展。在技术快速演进的当下，本综述旨在全面回顾基于深度学习的RSOD方法的最新成果，涵盖超过300篇论文。我们识别出RSOD中的五大主要挑战，包括多尺度目标检测、旋转目标检测、弱目标检测、小目标检测以及弱监督目标检测，并通过层级划分的方式系统性地梳理了相应的解决方法。此外，我们还综述了RSOD领域广泛使用的基准数据集与评估指标，以及RSOD的应用场景。最后，我们提出了未来研究方向，以进一步推动RSOD研究的发展。