Recently, numerous methods have achieved impressive performance in remote sensing object detection, relying on convolution or transformer architectures. Such detectors typically have a feature backbone to extract useful features from raw input images. For the remote sensing domain, a common practice among current detectors is to initialize the backbone with pre-training on ImageNet consisting of natural scenes. Fine-tuning the backbone is then typically required to generate features suitable for remote-sensing images. However, this could hinder the extraction of basic visual features in long-term training, thus restricting performance improvement. To mitigate this issue, we propose a novel method named DBF (Dynamic Backbone Freezing) for feature backbone fine-tuning on remote sensing object detection. Our method aims to handle the dilemma of whether the backbone should extract low-level generic features or possess specific knowledge of the remote sensing domain, by introducing a module called 'Freezing Scheduler' to dynamically manage the update of backbone features during training. Extensive experiments on DOTA and DIOR-R show that our approach enables more accurate model learning while substantially reducing computational costs. Our method can be seamlessly adopted without additional effort due to its straightforward design.

翻译：近年来，众多基于卷积或Transformer架构的方法在遥感目标检测中取得了令人瞩目的性能。此类检测器通常配备特征骨干网络，用于从原始输入图像中提取有用特征。在遥感领域，当前检测器的普遍做法是使用包含自然场景的ImageNet数据集进行预训练来初始化骨干网络。随后通常需要对骨干网络进行微调，以生成适用于遥感图像的特征。然而，在长期训练过程中，这可能会阻碍基础视觉特征的提取，从而限制性能提升。为缓解此问题，我们提出了一种名为DBF（动态骨干网络冻结）的新方法，用于遥感目标检测中的特征骨干网络微调。我们的方法通过引入名为“冻结调度器”的模块，在训练过程中动态管理骨干网络特征的更新，旨在解决骨干网络应提取低层通用特征还是具备遥感领域特定知识的两难问题。在DOTA和DIOR-R数据集上的大量实验表明，我们的方法在显著降低计算成本的同时，实现了更精确的模型学习。由于其简洁的设计，本方法无需额外工作即可无缝集成使用。