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}.

翻译：摘要：旋转目标检测在从航拍图像到自动驾驶的众多应用中涌现，而现有许多检测基准仅采用水平边界框标注（其成本低于精细旋转框），这导致易获取的训练语料与日益增长的旋转目标检测需求之间存在鸿沟。本文提出一种名为H2RBox的简单而有效的旋转目标检测方法，仅使用水平框标注进行弱监督训练，填补了上述鸿沟，其性能甚至可与采用旋转框训练的检测器相媲美。该方法的核心是弱监督与自监督学习，通过学习两种不同视角的一致性来预测目标角度。据我们所知，H2RBox是首个基于水平框标注的旋转目标检测器。与另一种替代方案（即水平框监督的实例分割后经适配用于旋转目标检测）相比，本方法不易受掩膜预测质量的影响，且在包含大量密集目标和异常值的复杂场景中更具鲁棒性。实验结果表明，H2RBox在性能和速度上显著优于水平框监督的实例分割方法，且内存需求更低；同时与旋转框监督的旋转目标检测器相比，本方法在性能和速度上非常接近。源代码已开源：基于PyTorch的MMRotate（https://github.com/yangxue0827/h2rbox-mmrotate）和基于Jittor的JDet（https://github.com/yangxue0827/h2rbox-jittor）。