Remote sensing target detection aims to identify and locate critical targets
within remote sensing images, finding extensive applications in agriculture and
urban planning. Feature pyramid networks (FPNs) are commonly used to extract
multi-scale features. However, existing FPNs often overlook extracting
low-level positional information and fine-grained context interaction. To
address this, we propose a novel location refined feature pyramid network
(LR-FPN) to enhance the extraction of shallow positional information and
facilitate fine-grained context interaction. The LR-FPN consists of two primary
modules: the shallow position information extraction module (SPIEM) and the
contextual interaction module (CIM). Specifically, SPIEM first maximizes the
retention of solid location information of the target by simultaneously
extracting positional and saliency information from the low-level feature map.
Subsequently, CIM injects this robust location information into different
layers of the original FPN through spatial and channel interaction, explicitly
enhancing the object area. Moreover, in spatial interaction, we introduce a
simple local and non-local interaction strategy to learn and retain the
saliency information of the object. Lastly, the LR-FPN can be readily
integrated into common object detection frameworks to improve performance
significantly. Extensive experiments on two large-scale remote sensing datasets
(i.e., DOTAV1.0 and HRSC2016) demonstrate that the proposed LR-FPN is superior
to state-of-the-art object detection approaches. Our code and models will be
publicly available.

提出了一种新颖的位置精化特征金字塔网络（LR-FPN），以增强浅层位置信息的提取和细粒度上下文交互。通过实验验证，LR-FPN 在遥感目标检测方面优于现有的方法。