Adaptive Feature Refinement for Oriented Object Detection in Remote Sensing Images

Object detection is an important and challenging task in remote sensing research. Remote sensing images are mostly taken from a top-down perspective. Due to their complex background and arbitrary orientation, object detection algorithms in natural scenes face some challenges when directly applied to remote sensing. Aiming at the above problems, this paper proposes an adaptive feature refinement network AFR- Net to generate directed candidate boxes with high matching degree to objects. Firstly, the feature enhancement module is designed to increase the feature representation with discriminative power, so as to improve the ability of capturing spatial details in complex background. Secondly, in order to obtain the directed candidate box adapted to the object direction, an adaptive feature alignment module is proposed to alleviate the spatial misalignment problem between convolution feature and directed objects, and the rotation- invariant feature is obtained. Finally, the rotation sensitive features are obtained by decoupling detection head module and accurate bounding box regression is refined. The proposed network achieves 66.71% and 97.12% accuracy in the publicly available remote sensing object detection datasets DIOR- R and HRSC2016, which are 2.3 and 0.9 percentage points higher than the original algorithm, respectively. At the same time, compared with some mainstream object detection algorithms, the proposed network has certain advantages. © 2018 Editorial Office Of Water Saving Irrigation. All rights reserved.