  Oriented object detection emerges in many applications from aerial images to
autonomous driving, while many existing detection benchmarks are annotated with
horizontal bounding box only which is also less costive than fine-grained
rotated box, leading to a gap between the readily available training corpus and
the rising demand for oriented object detection. This paper proposes a simple
yet effective oriented object detection approach called H2RBox merely using
horizontal box annotation for weakly-supervised training, which closes the
above gap and shows competitive performance even against those trained with
rotated boxes. The cores of our method are weakly- and self-supervised
learning, which predicts the angle of the object by learning the consistency of
two different views. To our best knowledge, H2RBox is the first horizontal box
annotation-based oriented object detector. Compared to an alternative i.e.
horizontal box-supervised instance segmentation with our post adaption to
oriented object detection, our approach is not susceptible to the prediction
quality of mask and can perform more robustly in complex scenes containing a
large number of dense objects and outliers. Experimental results show that
H2RBox has significant performance and speed advantages over horizontal
box-supervised instance segmentation methods, as well as lower memory
requirements. While compared to rotated box-supervised oriented object
detectors, our method shows very close performance and speed. The source code
is available at PyTorch-based
\href{https://github.com/yangxue0827/h2rbox-mmrotate}{MMRotate} and
Jittor-based \href{https://github.com/yangxue0827/h2rbox-jittor}{JDet}.
