Abstract
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.
Abstract (translated)
目前，远红外图像（RSI）中的少样本目标检测（FSOD）任务已成为一个关注点。许多基于两阶段检测的少样本检测器在处理RSI中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了不切实际的特征，主要原因是他们在处理大量数据时的松散模型参数。相比之下，我们认识到一阶段检测器的优势，包括高检测速度和全局接收视野。因此，我们选择YOLOv7作为 baseline，并将其置于一种新的元学习训练框架中。这种转换使检测器能够有效地处理FSOD任务，同时充分利用其轻量化的优势。此外，我们深入研究了元学习策略生成的样本，并引入了一种新的元抽样方法，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，我们故意利用经常被忽视的“负样本”来提取有价值的信息。这种方法旨在提高检测精度并有效地优化整个元学习策略。为了验证我们提出的检测器的有效性，我们使用DIOR和NWPU VHR-10.v2数据集与当前最先进的检测器进行性能比较，得到满意的结果。
URL
https://arxiv.org/abs/2404.18426
