  We present a new approach for oriented object detection, an anchor-free
one-stage detector. This approach, named TricubeNet, represents each object as
a 2D Tricube kernel and extracts bounding boxes using appearance-based
post-processing. Unlike existing anchor-based oriented object detectors, we can
save the computational complexity and the number of hyperparameters by
eliminating the anchor box in the network design. In addition, by adopting a
heatmap-based detection process instead of the box offset regression, we simply
and effectively solve the angle discontinuity problem, which is one of the
important problems for oriented object detection. To further boost the
performance, we propose some effective techniques for the loss balancing,
extracting the rotation-invariant feature, and heatmap refinement. To
demonstrate the effectiveness of our TricueNet, we experiment on various tasks
for the weakly-occluded oriented object detection. The extensive experimental
results show that our TricueNet is highly effective and competitive for
oriented object detection. The code is available at
https://github.com/qjadud1994/TricubeNet.
