  Objects in aerial images usually have arbitrary orientations and are densely
located over the ground, making them extremely challenge to be detected. Many
recently developed methods attempt to solve these issues by estimating an extra
orientation parameter and placing dense anchors, which will result in high
model complexity and computational costs. In this paper, we propose an
arbitrary-oriented region proposal network (AO-RPN) to generate oriented
proposals transformed from horizontal anchors. The AO-RPN is very efficient
with only a few amounts of parameters increase than the original RPN.
Furthermore, to obtain accurate bounding boxes, we decouple the detection task
into multiple subtasks and propose a multi-head network to accomplish them.
Each head is specially designed to learn the features optimal for the
corresponding task, which allows our network to detect objects accurately. We
name it MRDet short for Multi-head Rotated object Detector for convenience. We
test the proposed MRDet on two challenging benchmarks, i.e., DOTA and HRSC2016,
and compare it with several state-of-the-art methods. Our method achieves very
promising results which clearly demonstrate its effectiveness.
