  The detection of oriented objects is frequently appeared in the field of
natural scene text detection as well as object detection in aerial images.
Traditional detectors for oriented objects are common to rotate anchors on the
basis of the RCNN frameworks, which will multiple the number of anchors with a
variety of angles, coupled with rotating NMS algorithm, the computational
complexities of these models are greatly increased. In this paper, we propose a
novel model named Oriented Objects Detection Network O^2-DNet to detect
oriented objects by predicting a pair of middle lines inside each target.
O^2-DNet is an one-stage, anchor-free and NMS-free model. The target line
segments of our model are defined as two corresponding middle lines of original
rotating bounding box annotations which can be transformed directly instead of
additional manual tagging. Experiments show that our O^2-DNet achieves
excellent performance on ICDAR 2015 and DOTA datasets. It is noteworthy that
the objects in COCO can be regard as a special form of oriented objects with an
angle of 90 degrees. O^2-DNet can still achieve competitive results in these
general natural object detection datasets.
