Presently, the task of few-shot object detection (FSOD) in remote sensing images (RSIs) has become a focal point of attention. Numerous few-shot detectors, particularly those based on two-stage detectors, face challenges when dealing with the multiscale complexities inherent in RSIs. Moreover, these detectors present impractical characteristics in real-world applications, mainly due to their unwieldy model parameters when handling large amount of data. In contrast, we recognize the advantages of one-stage detectors, including high detection speed and a global receptive field. Consequently, we choose the YOLOv7 one-stage detector as a baseline and subject it to a novel meta-learning training framework. This transformation allows the detector to adeptly address FSOD tasks while capitalizing on its inherent advantage of lightweight. Additionally, we thoroughly investigate the samples generated by the meta-learning strategy and introduce a novel meta-sampling approach to retain samples produced by our designed meta-detection head. Coupled with our devised meta-cross loss, we deliberately utilize "negative samples" that are often overlooked to extract valuable knowledge from them. This approach serves to enhance detection accuracy and efficiently refine the overall meta-learning strategy. To validate the effectiveness of our proposed detector, we conducted performance comparisons with current state-of-the-art detectors using the DIOR and NWPU VHR-10.v2 datasets, yielding satisfactory results.

翻译：当前，遥感图像中的小样本目标检测任务已成为研究热点。众多小样本检测器，特别是基于两阶段检测器的方法，在处理遥感图像固有的多尺度复杂性时面临挑战。此外，这些检测器在实际应用中表现出不实用的特性，主要源于处理海量数据时模型参数量庞大。相比之下，我们认识到单阶段检测器具有检测速度快和全局感受野等优势。因此，我们选择YOLOv7单阶段检测器作为基线，并将其置于新颖的元学习训练框架中。这一改造使检测器能够充分利用其固有的轻量化优势，同时胜任小样本目标检测任务。此外，我们深入研究了元学习策略生成的样本，并提出一种新颖的元采样方法，以保留由我们设计的元检测头产生的样本。结合我们设计的元交叉损失函数，我们刻意利用常被忽视的"负样本"从中提取有价值的知识。该方法有助于提升检测精度，并有效优化整体元学习策略。为验证所提检测器的有效性，我们在DIOR和NWPU VHR-10.v2数据集上与当前最先进的检测器进行了性能对比，取得了令人满意的结果。