  We propose a novel method for representing oriented objects in aerial images
named Adaptive Period Embedding (APE). While traditional object detection
methods represent object with horizontal bounding boxes, the objects in aerial
images are oritented. Calculating the angle of object is an yet challenging
task. While almost all previous object detectors for aerial images directly
regress the angle of objects, they use complex rules to calculate the angle,
and their performance is limited by the rule design. In contrast, our method is
based on the angular periodicity of oriented objects. The angle is represented
by two two-dimensional periodic vectors whose periods are different, the vector
is continuous as shape changes. The label generation rule is more simple and
reasonable compared with previous methods. The proposed method is general and
can be applied to other oriented detector. Besides, we propose a novel IoU
calculation method for long objects named length independent IoU (LIIoU). We
intercept part of the long side of the target box to get the maximum IoU
between the proposed box and the intercepted target box. Thereby, some long
boxes will have corresponding positive samples. Our method reaches the 1st
place of DOAI2019 competition task1 (oriented object) held in workshop on
Detecting Objects in Aerial Images in conjunction with IEEE CVPR 2019.
