面向深度学习的遥感图像旋转目标检测研究综述

由于遥感图像中的目标具有方向任意、分布密集和尺度差异大等特点，使得遥感图像目标检测成为一个颇具挑战性的难题。针对该难题，系统梳理了近三年来深度学习遥感图像旋转目标检测的相关工作，首先介绍旋转框的表示方法及其特点；然后按照特征提取网络、旋转锚框和候选框生成、标签分配及采样策略、损失函数四个方面对当前遥感图像旋转目标检测的现有方法进行分析；再对常用的遥感图像旋转目标数据集进行介绍，对比分析不同算法的性能；最后对遥感图像旋转目标检测进行了展望。

Overview of deep learning oriented rotating object detection in remote sensing images

Since the objects in remote sensing images have the characteristics of arbitrary direction, dense distribution and large scale differences, object detection in remote sensing images has become a challenging problem. Aiming at this problem, this paper systematically reviewed the work related to rotating object detection in deep learning remote sensing images in recent three years. Firstly, this paper introduced the representation method and characteristics of rotated bounding box. Then, it analyzed the existing methods of rotating object detection in remote sensing images from four aspects: the feature extraction network, the generation of rotating anchor and candidate bounding box, the label allocation and sampling strategy, and the loss function. Next, it introduced the commonly used remote sensing image datasets for rotated object detection, and compared the performance of different algorithms. Finally, this paper prospected rotating object detection in remote sensing images.