  Arbitrary-oriented objects widely appear in natural scenes, aerial
photographs, remote sensing images, etc., thus arbitrary-oriented object
detection has received considerable attention. Many current rotation detectors
use plenty of anchors with different orientations to achieve spatial alignment
with ground truth boxes, then Intersection-over-Union (IoU) is applied to
sample the positive and negative candidates for training. However, we observe
that the selected positive anchors cannot always ensure accurate detections
after regression, while some negative samples can achieve accurate
localization. It indicates that the quality assessment of anchors through IoU
is not appropriate, and this further lead to inconsistency between
classification confidence and localization accuracy. In this paper, we propose
a dynamic anchor learning (DAL) method, which utilizes the newly defined
matching degree to comprehensively evaluate the localization potential of the
anchors and carry out a more efficient label assignment process. In this way,
the detector can dynamically select high-quality anchors to achieve accurate
object detection, and the divergence between classification and regression will
be alleviated. With the newly introduced DAL, we achieve superior detection
performance for arbitrary-oriented objects with only a few horizontal preset
anchors. Experimental results on three remote sensing datasets HRSC2016, DOTA,
UCAS-AOD as well as a scene text dataset ICDAR 2015 show that our method
achieves substantial improvement compared with the baseline model. Besides, our
approach is also universal for object detection using horizontal bound box. The
code and models are available at https://github.com/ming71/DAL.
