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.

遥感物体检测是遥感领域中最基本和具有挑战性的任务之一，深度学习技术在近年来展现出强大的特征表示能力，并推动了遥感物体检测技术的巨大飞跃。本综述旨在全面回顾基于深度学习的遥感物体检测方法的最新成果，涵盖了 300 多篇论文。我们在遥感物体检测中确定了五个主要挑战，包括多尺度物体检测、旋转目标检测、弱目标检测、微小目标检测和受限监督目标检测，并按照层次划分的方式系统回顾了相应的方法。我们还回顾了遥感物体检测领域中广泛使用的基准数据集和评估指标，以及遥感物体检测的应用场景。为进一步推动遥感物体检测研究提供了未来的研究方向。