1. 基于深度学习的遥感图像旋转目标检测研究综述.
- Author
-
陈天鹏 and 胡建文
- Subjects
- *
CONVOLUTIONAL neural networks , *OBJECT recognition (Computer vision) , *DEEP learning , *REMOTE sensing - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF