At present, few-shot object detection research in the field of optical remote sensing images has been conducted, but few-shot object detection in the field of synthetic aperture radar (SAR) images has rarely been explored. To this end, this article proposes a lightweight metalearning-based SAR image few-shot object detection method, which improves the accuracy and speed of SAR image few-shot object detection from a more balanced perspective. First, we introduce the latest FSODM method in optical remote sensing as a benchmark framework. Second, a lightweight metafeature extractor named DarknetS is designed to enhance the feature representation of SAR images and improve detection timeliness. Furthermore, we build a new aggregation module called AggregationS, which encodes support features and query features into the same feature subspace via a novel transformer encoder. This module design can better extract the correlation and saliency between different classes in the support set, improve the detection accuracy of the query set, and enhance the detection generalization performance of new classes. Finally, we built several real-world SAR image few-shot object detection datasets to verify the effectiveness of the method. Experimental results show that FSODS can achieve a better object detection performance compared to the baseline model under the condition that only a small amount of labeled data is required for new classes of SAR image objects.

中文翻译：

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FSODS：一种轻量级元学习方法，用于 SAR 图像上的少镜头目标检测

目前，光学遥感图像领域的小样本目标检测研究已经开展，但合成孔径雷达（SAR）图像领域的小样本目标检测研究很少。为此，本文提出了一种基于轻量级元学习的SAR图像少拍目标检测方法，从更平衡的角度提高了SAR图像少拍目标检测的准确性和速度。首先，我们介绍了光学遥感中最新的 FSODM 方法作为基准框架。其次，一种名为 DarknetS 的轻量级元特征提取器旨在增强 SAR 图像的特征表示并提高检测时效性。此外，我们构建了一个名为 AggregationS 的新聚合模块，它通过一种新颖的转换器编码器将支持特征和查询特征编码到相同的特征子空间中。这种模块设计可以更好地提取支持集中不同类之间的相关性和显着性，提高查询集的检测精度，增强新类的检测泛化性能。最后，我们构建了几个真实世界的 SAR 图像小样本目标检测数据集来验证该方法的有效性。实验结果表明，在新类别的 SAR 图像对象只需要少量标记数据的情况下，FSODS 与基线模型相比可以实现更好的目标检测性能。提高查询集的检测精度，增强新类的检测泛化性能。最后，我们构建了几个真实世界的 SAR 图像小样本目标检测数据集来验证该方法的有效性。实验结果表明，在新类别的 SAR 图像对象只需要少量标记数据的情况下，FSODS 与基线模型相比可以实现更好的目标检测性能。提高查询集的检测精度，增强新类的检测泛化性能。最后，我们构建了几个真实世界的 SAR 图像小样本目标检测数据集来验证该方法的有效性。实验结果表明，在新类别的 SAR 图像对象只需要少量标记数据的情况下，FSODS 与基线模型相比可以实现更好的目标检测性能。