Rotated object detectors commonly encounter instability during the training process, primarily due to background noise and angular periodicity. Targets with elongated or nonconvex shapes may introduce background noise during convolution, hindering accurately extracting features. Meanwhile, the periodicity of angles leads to predictions beyond the defined range, subsequently impeding the convergence. This letter introduces an angle adaptive module (AAM) designed for the backbone, enhancing the ability of the model to accurately extract object features and dynamically select the optimal angle. Moreover, to mitigate the effect of angle periodicity, a method called radian regression method (RRM) is proposed for predicting proper angles. It avoids directly regressing the value and instead produces the probability density distribution of the offset. We elaborately design numerous experiments to demonstrate the effectiveness of the proposed modules. As a result, the proposed method attains competitive results across various datasets, including DOTAv1.0, DOTAv1.5, DOTAv2.0, HRSC2016, and DIOR-R.

中文翻译：

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用于旋转物体检测的自适应角度模块和弧度回归方法

旋转物体检测器在训练过程中通常会遇到不稳定，这主要是由于背景噪声和角度周期性造成的。具有细长或非凸形状的目标可能会在卷积过程中引入背景噪声，从而妨碍准确提取特征。同时，角度的周期性导致预测超出定义的范围，从而阻碍收敛。这封信介绍了为骨干网络设计的角度自适应模块（AAM），增强了模型准确提取物体特征并动态选择最佳角度的能力。此外，为了减轻角度周期性的影响，提出了一种称为弧度回归法（RRM）的方法来预测适当的角度。它避免直接对值进行回归，而是生成偏移量的概率密度分布。我们精心设计了大量实验来证明所提出模块的有效性。因此，所提出的方法在各种数据集（包括 DOTAv1.0、DOTAv1.5、DOTAv2.0、HRSC2016 和 DIOR-R）上获得了有竞争力的结果。