Adaptive Period Embedding for Representing Oriented Objects in Aerial Images
Yixing Zhu, Jun Du, Xueqing Wu
Abstract
We propose a novel method for representing oriented objects in aerial images named adaptive period embedding (APE). Although traditional object detection methods represent objects using horizontal bounding boxes, the objects in aerial images are oriented. Calculating the angle of the object is a yet challenging task. 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 2-D periodic vectors the periods of which are different, so the vector is continuous as the shape changes. The label generation rule is simpler and more reasonable compared with previous methods. The proposed method is general and can be applied to other oriented detector. Besides, we propose a novel intersection over union (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 intercepted target box. Thereby, some long boxes will have corresponding positive samples. Our method reaches the first place of DOAI2019 competition task1 (oriented object) held in a workshop on detecting objects in aerial images in conjunction with IEEE CVPR 2019.