In the wake of developments in remote sensing, the application of target detection of remote sensing is of increasing interest. Unfortunately, unlike natural image processing, remote sensing image processing involves dealing with large variations in object size, which poses a great challenge to researchers. Although traditional multi-scale detection networks have been successful in solving problems with such large variations, they still have certain limitations: (1) The traditional multi-scale detection methods note the scale of features but ignore the correlation between feature levels. Each feature map is represented by a single layer of the backbone network, and the extracted features are not comprehensive enough. For example, the SSD network uses the features extracted from the backbone network at different scales directly for detection, resulting in the loss of a large amount of contextual information. (2) These methods combine with inherent backbone classification networks to perform detection tasks. RetinaNet is just a combination of the ResNet-101 classification network and FPN network to perform the detection tasks; however, there are differences in object classification and detection tasks. To address these issues, a cross-scale feature fusion pyramid network (CF2PN) is proposed. First and foremost, a cross-scale fusion module (CSFM) is introduced to extract sufficiently comprehensive semantic information from features for performing multi-scale fusion. Moreover, a feature pyramid for target detection utilizing thinning U-shaped modules (TUMs) performs the multi-level fusion of the features. Eventually, a focal loss in the prediction section is used to control the large number of negative samples generated during the feature fusion process. The new architecture of the network proposed in this paper is verified by DIOR and RSOD dataset. The experimental results show that the performance of this method is improved by 2%-12% in the DIOR dataset and RSOD dataset compared with the current SOTA target detection methods.

中文翻译：

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CF2PN：基于跨尺度特征融合金字塔网络的遥感目标检测

随着遥感技术的发展，遥感目标检测的应用越来越受到关注。不幸的是，与自然图像处理不同，遥感图像处理涉及处理对象大小的较大变化，这给研究人员带来了巨大挑战。尽管传统的多尺度检测网络已经成功地解决了变化较大的问题，但是它们仍然具有一定的局限性：（1）传统的多尺度检测方法注意到特征的尺度，却忽略了特征层次之间的相关性。每个特征图由骨干网的单个层表示，并且提取的特征不够全面。例如，SSD网络直接使用从骨干网络中提取的不同比例的特征进行检测，导致丢失大量上下文信息。（2）这些方法结合固有的骨干分类网络来执行检测任务。RetinaNet只是ResNet-101分类网络和FPN网络的组合来执行检测任务；但是，对象分类和检测任务有所不同。为了解决这些问题，提出了跨尺度特征融合金字塔网络（CF2PN）。首先，最重要的是，引入了跨尺度融合模块（CSFM），以从用于执行多尺度融合的特征中提取足够全面的语义信息。此外，利用变薄U形模块（TUM）进行目标检测的特征金字塔执行了特征的多级融合。最终，预测部分中的焦点损失用于控制特征融合过程中生成的大量负样本。通过DIOR和RSOD数据集验证了本文提出的新网络架构。实验结果表明，与目前的SOTA目标检测方法相比，该方法在DIOR数据集和RSOD数据集中的性能提高了2％-12％。