  We classify the discontinuity of loss in both five-param and eight-param
rotated object detection methods as rotation sensitivity error (RSE) which will
result in performance degeneration. We introduce a novel modulated rotation
loss to alleviate the problem and propose a rotation sensitivity detection
network (RSDet) which is consists of an eight-param single-stage rotated object
detector and the modulated rotation loss. Our proposed RSDet has several
advantages: 1) it reformulates the rotated object detection problem as
predicting the corners of objects while most previous methods employ a
five-para-based regression method with different measurement units. 2)
modulated rotation loss achieves consistent improvement on both five-param and
eight-param rotated object detection methods by solving the discontinuity of
loss. To further improve the accuracy of our method on objects smaller than 10
pixels, we introduce a novel RSDet++ which is consists of a point-based
anchor-free rotated object detector and a modulated rotation loss. Extensive
experiments demonstrate the effectiveness of both RSDet and RSDet++, which
achieve competitive results on rotated object detection in the challenging
benchmarks DOTA1.0, DOTA1.5, and DOTA2.0. We hope the proposed method can
provide a new perspective for designing algorithms to solve rotated object
detection and pay more attention to tiny objects. The codes and models are
available at: https://github.com/yangxue0827/RotationDetection.

Computer Vision and Pattern Recognition