Detection pre-training methods for the DETR series detector have been extensively studied in natural scenes, e.g., DETReg. However, the detection pre-training remains unexplored in remote sensing scenes. In existing pre-training methods, alignment between object embeddings extracted from a pre-trained backbone and detector features is significant. However, due to differences in feature extraction methods, a pronounced feature discrepancy still exists and hinders the pre-training performance. The remote sensing images with complex environments and more densely distributed objects exacerbate the discrepancy. In this work, we propose a novel Mutually optimizing pre-training framework for remote sensing object Detection, dubbed as MutDet. In MutDet, we propose a systemic solution against this challenge. Firstly, we propose a mutual enhancement module, which fuses the object embeddings and detector features bidirectionally in the last encoder layer, enhancing their information interaction.Secondly, contrastive alignment loss is employed to guide this alignment process softly and simultaneously enhances detector features' discriminativity. Finally, we design an auxiliary siamese head to mitigate the task gap arising from the introduction of enhancement module. Comprehensive experiments on various settings show new state-of-the-art transfer performance. The improvement is particularly pronounced when data quantity is limited. When using 10% of the DIOR-R data, MutDet improves DetReg by 6.1% in AP50. Codes and models are available at: https://github.com/floatingstarZ/MutDet.

翻译：针对DETR系列检测器的检测预训练方法已在自然场景中得到广泛研究，例如DETReg。然而，在遥感场景中，检测预训练仍处于探索阶段。现有预训练方法中，从预训练骨干网络提取的目标嵌入与检测器特征之间的对齐至关重要。但由于特征提取方法的差异，显著的特征差异仍然存在并阻碍预训练性能。具有复杂环境和更密集分布目标的遥感图像加剧了这种差异。本文提出一种新颖的面向遥感目标检测的互优化预训练框架，命名为MutDet。在MutDet中，我们针对这一挑战提出了系统性解决方案。首先，我们提出互增强模块，在最后一个编码器层中双向融合目标嵌入与检测器特征，增强其信息交互。其次，采用对比对齐损失以柔性方式指导对齐过程，同时提升检测器特征的判别性。最后，我们设计辅助孪生头以缓解因引入增强模块而产生的任务差距。多种设置下的综合实验展示了新的最先进迁移性能。在数据量有限时改进尤为显著：当使用10% DIOR-R数据时，MutDet将DetReg的AP50指标提升了6.1%。代码与模型发布于：https://github.com/floatingstarZ/MutDet。