Arbitrary-oriented objects exist widely in natural scenes, and thus the oriented object detection has received extensive attention in recent years. The mainstream rotation detectors use oriented bounding boxes (OBB) or quadrilateral bounding boxes (QBB) to represent the rotating objects. However, these methods suffer from the representation ambiguity for oriented object definition, which leads to suboptimal regression optimization and the inconsistency between the loss metric and the localization accuracy of the predictions. In this paper, we propose a Representation Invariance Loss (RIL) to optimize the bounding box regression for the rotating objects. Specifically, RIL treats multiple representations of an oriented object as multiple equivalent local minima, and hence transforms bounding box regression into an adaptive matching process with these local minima. Then, the Hungarian matching algorithm is adopted to obtain the optimal regression strategy. We also propose a normalized rotation loss to alleviate the weak correlation between different variables and their unbalanced loss contribution in OBB representation. Extensive experiments on remote sensing datasets and scene text datasets show that our method achieves consistent and substantial improvement. The source code and trained models are available at https://github.com/ming71/RIDet.
翻译:自然场景中广泛存在任意定向物体,因此,定向天体探测近年来受到广泛关注。主流旋转探测器使用定向约束箱(OBB)或四边约束箱(QBB)代表旋转对象。但是,这些方法因定向天体定义的表述含糊不清而受到影响,导致偏差回归优化,以及损失衡量标准与预测的局部化精确度之间不一致。在本文件中,我们提议采用“代表不易损失”(RIL)优化旋转对象的捆绑框回归。具体来说,里拉将定向天体的多重表示作为多重等效本地微型,从而将捆绑框回归转化为与这些本地小型天体的适应匹配进程。随后,匈牙利匹配算法被采用,以获得最佳回归战略。我们还提议采用正常的旋转损失,以缓解不同变量之间薄弱的关联性及其在OBB中不平衡的损失贡献。关于遥感数据集和场景文本数据集的广泛实验表明,我们的方法取得了一致和实质性的改进。源码和经过培训的模型可在 https://github.com/ming.71/ming查阅。