Arbitrary-oriented object detection is a relatively emerging but challenging task. Although remarkable progress has been made, there still remain many unsolved issues due to the large diversity of patterns in orientation, scale, aspect ratio, and visual appearance of objects in aerial images. Most of the existing methods adopt a coarse-grained fixed label assignment strategy and suffer from the inconsistency between the classification score and localization accuracy. First, to align the metric inconsistency between sample selection and regression loss calculation caused by fixed IoU strategy, we introduce affine transformation to evaluate the quality of samples and propose a distance-based label assignment strategy. The proposed metric-aligned selection (MAS) strategy can dynamically select samples according to the shape and rotation characteristic of objects. Second, to further address the inconsistency between classification and localization, we propose a critical feature sampling (CFS) module, which performs localization refinement on the sampling location for classification task to extract critical features accurately. Third, we present a scale-controlled smooth $L_1$ loss (SC-Loss) to adaptively select high quality samples by changing the form of regression loss function based on the statistics of proposals during training. Extensive experiments are conducted on four challenging rotated object detection datasets DOTA, FAIR1M-1.0, HRSC2016, and UCAS-AOD. The results show the state-of-the-art accuracy of the proposed detector.

翻译：朝向任意目标检测是一项相对新兴但具有挑战性的任务。尽管已取得显著进展，但由于航拍图像中目标在方向、尺度、长宽比和视觉外观上存在极大多样性，仍有许多未解决的问题。现有方法大多采用粗粒度的固定标签分配策略，且面临分类分数与定位精度不一致的问题。首先，为消除固定IoU策略导致的样本选择与回归损失计算之间的度量不一致性，我们引入仿射变换评估样本质量，并提出基于距离的标签分配策略。所提出的度量对齐选择（MAS）策略能够根据目标的形状和旋转特征动态选择样本。其次，为进一步解决分类与定位的不一致性，我们提出关键特征采样（CFS）模块，该模块对分类任务的采样位置进行定位精细化处理，以精确提取关键特征。第三，我们提出尺度可控平滑$L_1$损失（SC-Loss），通过基于训练过程中候选框的统计量改变回归损失函数形式，自适应地选择高质量样本。在四个具有挑战性的旋转目标检测数据集DOTA、FAIR1M-1.0、HRSC2016和UCAS-AOD上进行了大量实验。结果表明，所提出的检测器达到了最先进的精度。