Learning a Rotation Invariant Detector with Rotatable Bounding Box

TL;DR

This paper introduces DRBox, a rotation-invariant object detector using rotatable bounding boxes, which improves detection accuracy and orientation estimation for arbitrarily rotated objects in satellite images.

Contribution

The paper proposes a novel detection method with rotatable bounding boxes that effectively handles arbitrary object orientations, outperforming traditional methods.

Findings

DRBox outperforms Faster R-CNN and SSD on satellite images.

It is more robust to object and image rotation.

DRBox accurately outputs object orientation angles.

Abstract

Detection of arbitrarily rotated objects is a challenging task due to the difficulties of locating the multi-angle objects and separating them effectively from the background. The existing methods are not robust to angle varies of the objects because of the use of traditional bounding box, which is a rotation variant structure for locating rotated objects. In this article, a new detection method is proposed which applies the newly defined rotatable bounding box (RBox). The proposed detector (DRBox) can effectively handle the situation where the orientation angles of the objects are arbitrary. The training of DRBox forces the detection networks to learn the correct orientation angle of the objects, so that the rotation invariant property can be achieved. DRBox is tested to detect vehicles, ships and airplanes on satellite images, compared with Faster R-CNN and SSD, which are chosen as the benchmark of the traditional bounding box based methods. The results shows that DRBox performs much better than traditional bounding box based methods do on the given tasks, and is more robust against rotation of input image and target objects. Besides, results show that DRBox correctly outputs the orientation angles of the objects, which is very useful for locating multi-angle objects efficiently. The code and models are available at https://github.com/liulei01/DRBox.