Self-Adaptive Aspect Ratio Anchor for Oriented Object Detection in Remote Sensing Images

Object detection is a significant and challenging problem in the study of remote sensing. Since remote sensing images are typically captured with a bird's-eye view, the aspect ratios of objects in the same category may obey a Gaussian distribution. Generally, existing object detection methods ignore exploring the distribution character of aspect ratios for improving performance in remote sensing tasks. In this paper, we propose a novel Self-Adaptive Aspect Ratio Anchor (SARA) to explicitly explore aspect ratio variations of objects in remote sensing images. To be concrete, our SARA can self-adaptively learn an appropriate aspect ratio for each category. In this way, we can only utilize a simple squared anchor (related to the strides of feature maps in Feature Pyramid Networks) to regress objects in various aspect ratios. Finally, we adopt an Oriented Box Decoder (OBD) to align the feature maps and encode the orientation information of oriented objects. Our method achieves a promising mAP value of 79.91% on the DOTA dataset.

Automated summary

In this study, a novel Self-Adaptive Aspect Ratio Anchor (SARA) is proposed to explore aspect ratio variations of objects in remote sensing images, along with an Oriented Box Decoder (OBD) to encode orientation information of oriented objects. The method achieves a promising mAP value of 79.91% on the Dota dataset.