Adaptive Remote Sensing Image Attribute Learning for Active Object Detection

TL;DR

This paper introduces an active object detection approach for remote sensing images that uses deep reinforcement learning to adaptively adjust image attributes like brightness and scale, enhancing detection performance without retraining.

Contribution

It proposes a novel active detection framework that dynamically adjusts image attributes to improve detection accuracy in remote sensing images, addressing limitations of passive detection methods.

Findings

Improved detection performance through adaptive image attribute learning.

Enhanced image quality leads to better detection without retraining.

Demonstrated effectiveness on remote sensing datasets.

Abstract

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.