Object detection plays an important role in the field of remote sensing (RS) images analysis. The advancement of object detection task for RS images is extremely challenging due to object scale variation and complex background. Almost all detection frameworks use the neck network to fuse the feature maps extracted by the backbone to obtain better features, among which feature pyramid network (FPN) is the most widely used. Although traditional FPN has shown great potential in multi-scale object detection based on deep learning, it has unsatisfactory detection accuracy for small objects and confusing objects in RS images because of the lack of rich semantic and contextual information. We propose an architecture, called semantics reused context FPN that is portable to any FPN-based detectors to boost the detection performance in RS images without parameters increasing significantly. It includes two blocks, namely, context feature enhanced block, which uses dense connection and a learnable branch structure to extract rich context features with multiple receptive fields, and semantic feature reused block, which enhances semantic information of shallow feature maps by reusing later-layer features. Comprehensive evaluations on three benchmark datasets of geospatial object detection demonstrate that our method is superior to the existing methods.

中文翻译：

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语义重用上下文特征金字塔网络用于遥感图像中的目标检测

目标检测在遥感（RS）图像分析领域发挥着重要作用。由于目标尺度变化和复杂背景，RS 图像目标检测任务的推进极具挑战性。几乎所有的检测框架都使用颈部网络来融合主干提取的特征图以获得更好的特征，其中特征金字塔网络（FPN）应用最为广泛。虽然传统的FPN在基于深度学习的多尺度目标检测中显示出巨大的潜力，但由于缺乏丰富的语义和上下文信息，对于RS图像中的小目标和混淆目标的检测精度并不理想。我们提出一种架构，称为语义重用上下文 FPN，可移植到任何基于 FPN 的检测器，以提高 RS 图像中的检测性能，而不会显着增加参数。它包括两个块，即上下文特征增强块，它使用密集连接和可学习的分支结构来提取具有多个感受野的丰富上下文特征，以及语义特征重用块，它通过重用后层来增强浅层特征图的语义信息特征。对三个地理空间目标检测基准数据集的综合评估表明，我们的方法优于现有方法。它使用密集连接和可学习的分支结构来提取具有多个感受野的丰富上下文特征，以及语义特征重用块，通过重用后层特征来增强浅层特征图的语义信息。对三个地理空间目标检测基准数据集的综合评估表明，我们的方法优于现有方法。它使用密集连接和可学习的分支结构来提取具有多个感受野的丰富上下文特征，以及语义特征重用块，通过重用后层特征来增强浅层特征图的语义信息。对三个地理空间目标检测基准数据集的综合评估表明，我们的方法优于现有方法。