  Existing rotated object detectors are mostly inherited from the horizontal
detection paradigm, as the latter has evolved into a well-developed area.
However, these detectors are difficult to perform prominently in high-precision
detection due to the limitation of current regression loss design, especially
for objects with large aspect ratios. Taking the perspective that horizontal
detection is a special case for rotated object detection, in this paper, we are
motivated to change the design of rotation regression loss from induction
paradigm to deduction methodology, in terms of the relation between rotation
and horizontal detection. We show that one essential challenge is how to
modulate the coupled parameters in the rotation regression loss, as such the
estimated parameters can influence to each other during the dynamic joint
optimization, in an adaptive and synergetic way. Specifically, we first convert
the rotated bounding box into a 2-D Gaussian distribution, and then calculate
the Kullback-Leibler Divergence (KLD) between the Gaussian distributions as the
regression loss. By analyzing the gradient of each parameter, we show that KLD
(and its derivatives) can dynamically adjust the parameter gradients according
to the characteristics of the object. It will adjust the importance (gradient
weight) of the angle parameter according to the aspect ratio. This mechanism
can be vital for high-precision detection as a slight angle error would cause a
serious accuracy drop for large aspect ratios objects. More importantly, we
have proved that KLD is scale invariant. We further show that the KLD loss can
be degenerated into the popular $l_{n}$-norm loss for horizontal detection.
Experimental results on seven datasets using different detectors show its
consistent superiority, and codes are available at
https://github.com/yangxue0827/RotationDetection and
https://github.com/open-mmlab/mmrotate.
