Multioriented object detection, an important yet challenging task because of the bird's-eye-view perspective, complex background, and densely packed objects, is in the spotlight of detection in remote sensing images. Although existing methods have recently experienced substantial progress based on oriented head, they learn little about essential rotation invariance of the objects. In this article, a novel framework is proposed that can learn high-quality rotation invariance features of the multioriented objects by three measures. Given a remote sensing image, the multiscale semantic segmentation feature fusion module first merges the global semantic segmentation features predicted by the semantic segmentation branch and the multiscale features extracted by the backbone with FPN in order to distinguish complex background. Then, the discriminative features are used by rotation mainstream, whose structure is similar to cascade R-CNN and can extract higher quality rotation invariance features and predict more accurate location information by adaptively adjusting the distribution of the samples through progressive intersection over union thresholds. And in order to improve the performance of mainstream to predict more accurate oriented bounding box, the horizontal tributaries that can fully leverage the reciprocal relationship between the oriented detection and horizontal detection were added to the latter two stages. Extensive experiments on three public datasets for remote sensing images, i.e., Gaofen Airplane, HRSC2016, and DOTA demonstrate that without bells and whistles, the proposed method achieves superior performances compared with the existing state-of-the-art methods for multioriented detection. Moreover, our overall system achieves 59.264% mAP of airplane Detection in 2020 Gaofen challenge, ranking third in the final.

中文翻译：

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为遥感图像中的多向目标检测学习更高质量的旋转不变特征

多方位目标检测是一项重要但具有挑战性的任务，因为鸟瞰视角、复杂的背景和密集的目标，是遥感图像检测的焦点。尽管现有方法最近基于定向头部取得了实质性进展，但它们对物体的基本旋转不变性知之甚少。在本文中，提出了一种新颖的框架，可以通过三个度量来学习多向对象的高质量旋转不变性特征。给定一幅遥感图像，多尺度语义分割特征融合模块首先将语义分割分支预测的全局语义分割特征和骨干提取的多尺度特征与FPN进行融合，以区分复杂的背景。然后，旋转主流使用判别性特征，其结构类似于级联R-CNN，可以通过联合阈值上的渐进交叉自适应调整样本的分布来提取更高质量的旋转不变特征并预测更准确的位置信息。并且为了提高主流的性能以预测更准确的定向边界框，在后两个阶段添加了可以充分利用定向检测和水平检测之间相互关系的水平支路。在三个公共遥感图像数据集上的大量实验，即高分飞机、HRSC2016 和 DOTA 证明，没有花里胡哨，与现有的最先进的多方向检测方法相比，所提出的方法具有更好的性能。此外，我们的整体系统在 2020 年高分挑战中实现了 59.264% 的飞机检测 mAP，在决赛中排名第三。