  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.
