Remote sensing object detection (RSOD), one of the most fundamental and challenging tasks in the remote sensing field, has received long-standing 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 article 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 multiscale 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.

中文翻译：

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遥感目标检测与深度学习的结合：挑战和进步的元回顾

遥感目标检测（RSOD）是遥感领域最基本、最具挑战性的任务之一，长期以来受到人们的关注。近年来，深度学习技术展现出了强大的特征表示能力，带动了RSOD技术的发展飞跃。在这个技术快速发展的时代，本文旨在对基于深度学习的 RSOD 方法的最新成就进行全面回顾。本次综述涵盖了 300 多篇论文。我们确定了 RSOD 的五个主要挑战，包括多尺度目标检测、旋转目标检测、弱目标检测、微小目标检测和有限监督目标检测，并系统地回顾了以分层划分方式开发的相应方法。我们还回顾了 RSOD 领域广泛使用的基准数据集和评估指标以及 RSOD 的应用场景。为进一步推动RSOD的研究提供了未来的研究方向。