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.

翻译：空中图像中的物体通常具有任意方向,并且位于地上密密,因此极具挑战性。许多最近开发的方法试图通过估计额外方向参数和放置密集锚来解决这些问题,这将产生高模型复杂性和计算成本。在本文中,我们提议了一个任意方向的区域建议网络(AO-RPN),以产生从水平锚转换出来的面向方向的建议。AO-RPN非常高效,仅增加几倍参数,比原RPN增加一些参数。此外,为了获得精确的捆绑框,我们把探测任务分解成多个子任务,并提出完成这些任务的多头网络。每个头都专门设计来学习相应任务的最佳特征,从而使我们的网络能够准确探测物体。我们将其命名为多头旋转物体探测器的MRDet短,方便地。我们用两个挑战性基准,即DOTA和HRSC2016来测试拟议的MDet。我们的方法取得了非常有希望的结果,明确证明了它的有效性。