An Active Object-Detection Algorithm for Adaptive Attribute Adjustment of Remote-Sensing Images

In recent years, the continuous advancement of deep learning has led to significant progress in object-detection technology for remote-sensing images. However, most current detection methods passively perform detection on the input image without considering the relationship between imaging configurations and detection-algorithm performance. Therefore, when factors such as poor lighting conditions, extreme shooting angles, or long acquisition distances degrade image quality, the passive detection framework limits the effectiveness of the current detection algorithm, preventing it from completing the detection task. To address the limitations above, this paper proposes an active object-detection (AOD) method based on deep reinforcement learning, taking adaptive brightness and collection position adjustments as examples. Specifically, we first established an end-to-end network structure to generate attribute control policies. Then, we designed a reward function suitable for remote-sensing images based on the degree of improvement in detection performance. Finally, we propose a new viewpoint-management method in this paper, which is successfully implemented by a training method of long-term Prioritized Experience Replay (LPER), which significantly reduces the accumulation of negative and repetitive samples and improves the success rate of the AOD algorithm for remote-sensing images. The experiments on two public datasets have fully demonstrated the effectiveness and advantages of the algorithm proposed in this paper.