The paper introduces a novel Location Refined Feature Pyramid Network (LR-FPN) to enhance the extraction of shallow positional information and facilitate fine-grained context interaction in remote sensing object detection. LR-FPN consists of two primary modules: the Shallow Position Information Extraction Module (SPIEM) and the Contextual Interaction Module (CIM). SPIEM extracts positional and saliency information from low-level feature maps, while CIM injects this information into different layers of the original FPN through spatial and channel interaction, enhancing the object area. Extensive experiments on two large-scale remote sensing datasets (DOTA1.0 and HRSC2016) demonstrate that LR-FPN outperforms state-of-the-art object detection approaches, significantly improving performance in remote sensing scenes. The paper also includes ablation studies and visualizations to validate the effectiveness of the proposed modules.The paper introduces a novel Location Refined Feature Pyramid Network (LR-FPN) to enhance the extraction of shallow positional information and facilitate fine-grained context interaction in remote sensing object detection. LR-FPN consists of two primary modules: the Shallow Position Information Extraction Module (SPIEM) and the Contextual Interaction Module (CIM). SPIEM extracts positional and saliency information from low-level feature maps, while CIM injects this information into different layers of the original FPN through spatial and channel interaction, enhancing the object area. Extensive experiments on two large-scale remote sensing datasets (DOTA1.0 and HRSC2016) demonstrate that LR-FPN outperforms state-of-the-art object detection approaches, significantly improving performance in remote sensing scenes. The paper also includes ablation studies and visualizations to validate the effectiveness of the proposed modules.