  Oriented bounding box regression is crucial for oriented object detection.
However, regression-based methods often suffer from boundary problems and the
inconsistency between loss and evaluation metrics. In this paper, a modulated
Kalman IoU loss of approximate SkewIoU is proposed, named MKIoU. To avoid
boundary problems, we convert the oriented bounding box to Gaussian
distribution, then use the Kalman filter to approximate the intersection area.
However, there exists significant difference between the calculated and actual
intersection areas. Thus, we propose a modulation factor to adjust the
sensitivity of angle deviation and width-height offset to loss variation,
making the loss more consistent with the evaluation metric. Furthermore, the
Gaussian modeling method avoids the boundary problem but causes the angle
confusion of square objects simultaneously. Thus, the Gaussian Angle Loss (GA
Loss) is presented to solve this problem by adding a corrected loss for square
targets. The proposed GA Loss can be easily extended to other Gaussian-based
methods. Experiments on three publicly available aerial image datasets, DOTA,
UCAS-AOD, and HRSC2016, show the effectiveness of the proposed method.
