Label assignment is a crucial process in object detection, which significantly influences the detection performance by determining positive or negative samples during training process. However, existing label assignment strategies barely consider the characteristics of targets in remote sensing images (RSIs) thoroughly, e.g., large variations in scales and aspect ratios, leading to insufficient and imbalanced sampling and introducing more low-quality samples, thereby limiting detection performance. To solve the above problems, an Elliptical Distribution aided Adaptive Rotation Label Assignment (EARL) is proposed to select high-quality positive samples adaptively in anchor-free detectors. Specifically, an adaptive scale sampling (ADS) strategy is presented to select samples adaptively among multi-level feature maps according to the scales of targets, which achieves sufficient sampling with more balanced scale-level sample distribution. In addition, a dynamic elliptical distribution aided sampling (DED) strategy is proposed to make the sample distribution more flexible to fit the shapes and orientations of targets, and filter out low-quality samples. Furthermore, a spatial distance weighting (SDW) module is introduced to integrate the adaptive distance weighting into loss function, which makes the detector more focused on the high-quality samples. Extensive experiments on several popular datasets demonstrate the effectiveness and superiority of our proposed EARL, where without bells and whistles, it can be easily applied to different detectors and achieve state-of-the-art performance. The source code will be available at: https://github.com/Justlovesmile/EARL.

翻译：标签分配是目标检测中的关键过程，它通过在训练过程中确定正负样本来显著影响检测性能。然而，现有标签分配策略很少充分考虑遥感图像（RSIs）中目标的特性，例如尺度和长宽比的巨大差异，导致采样不足且不平衡，并引入了更多低质量样本，从而限制了检测性能。为解决上述问题，本文提出了一种椭圆分布辅助的自适应旋转标签分配方法（EARL），用于在无锚点检测器中自适应地选择高质量正样本。具体而言，提出了一种自适应尺度采样（ADS）策略，根据目标的尺度在多级特征图中自适应地选择样本，从而实现充分采样并具有更均衡的尺度级样本分布。此外，提出了一种动态椭圆分布辅助采样（DED）策略，使样本分布更加灵活以拟合目标的形状和方向，并过滤掉低质量样本。进一步，引入空间距离加权（SDW）模块，将自适应距离加权集成到损失函数中，从而使检测器更加关注高质量样本。在多个流行数据集上的大量实验证明了所提EARL的有效性和优越性，该方法无需复杂附加组件即可轻松应用于不同检测器，并实现了最先进的性能。源代码将在以下地址提供：https://github.com/Justlovesmile/EARL。