Detecting oriented objects in optical remote sensing images has been consistently challenging due to difficulties in bounding boxes’ localization. The cascaded regression framework, widely used for high-quality bounding box refinement, has demonstrated effectiveness in this domain. However, our experiments reveal a discontinuity issue in bounding box optimization in cascaded regression framework. As a result, performance gain is not guaranteed across all stages in this framework. In this article, we propose a distribution discriminative detector (DDDet) to address the above issues and enhance the optimization of bounding boxes in oriented object detection. Specifically, a novel conditional anchor refinement framework (CARF) is designed to improve cascaded regression structure. CARF distinguishes bounding boxes with different distributions, adaptively optimizing them within the well-assigned regressors. Subsequently, the aligned convolution module (ACM) is integrated into each regressor, facilitating the continuous alignment between features and refined anchors. Furthermore, the geometry-guided training sample selection (GTSS) method is incorporated into CARF to assign labels based on object shape priors. Experimental results show that DDDet obtains state-of-the-art performance on mainstream datasets for oriented object detection in remote sensing image, which demonstrates the effectiveness of the proposed method. Our method surpasses many current single-stage detectors, two-stage detectors, and refine-stage detectors, achieving the mAP of 79.41% on the DOTA dataset and 44.15% on the FAIR1M dataset.

中文翻译：

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并非所有框都相等：学习优化光学遥感图像中具有判别分布的边界框