Adaptive Remote Sensing Image Attribute Learning for Active Object Detection | |
Xu, Nuo4,5; Huo, Chunlei4,5; Guo, Jiacheng3; Liu, Yiwei2; Wang, Jian1; Pan, Chunhong4,5 | |
2021 | |
会议日期 | 2021.1.10-1.15 |
会议地点 | Milan, Italy |
英文摘要 | In recent years, deep learning methods bring incredible progress to the field of object detection. However, in the field of remote sensing image processing, existing methods neglect the relationship between imaging configuration and detection performance, and do not take into account the importance of detection performance feedback for improving image quality. Therefore, detection performance is limited by the passive nature of the conventional object detection framework. In order to solve the above limitations, this paper takes adaptive brightness adjustment and scale adjustment as examples, and proposes an active object detection method based on deep reinforcement learning. The goal of adaptive image attribute learning is to maximize the detection performance. With the help of active object detection and image attribute adjustment strategies, low-quality images can be converted into high-quality images, and the overall performance is improved without retraining the detector. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/50609] |
专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
通讯作者 | Huo, Chunlei |
作者单位 | 1.College of Robotics, Beijing Union University 2.Beijing University of Civil Engineering and Architecture 3.Beijing Information Science and Technology University 4.School of Artificial Intelligence, University of Chinese Academy of Sciences 5.NLPR, Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Xu, Nuo,Huo, Chunlei,Guo, Jiacheng,et al. Adaptive Remote Sensing Image Attribute Learning for Active Object Detection[C]. 见:. Milan, Italy. 2021.1.10-1.15. |
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