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
this https URL

提出了一种新的检测方法，应用了新定义的可旋转边界框（RBox），该方法可以有效地处理目标物体方向角任意的情况，并可以通过正确学习物体的正确方向角来实现旋转不变性。与基于传统边界框的方法相比，DRBox 在检测汽车、船舶和飞机的卫星图像方面表现更好，并且对输入图像和目标物体进行旋转更加稳健且能够正确输出其方向角，从而有助于高效定位多角度目标。