LR-FPN: Enhancing Remote Sensing Object Detection with Location Refined Feature Pyramid Network This paper proposes a novel Location Refined Feature Pyramid Network (LR-FPN) to enhance shallow positional information extraction 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 Context Interaction Module (CIM). SPIEM extracts positional and saliency information from low-level feature maps to maintain accurate target location information. CIM infuses robust location information into different layers of the original FPN through spatial and channel interaction, enhancing the object area. 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 (DOTAV1.0 and HRSC2016) demonstrate that the proposed LR-FPN is superior to state-of-the-art object detection approaches. The code and models will be publicly available. The main contributions of this paper are: (1) presenting a plug-and-play location refined feature pyramid network (LR-FPN) to enhance the extraction of shallow positional information and facilitate fine-grained context interaction; (2) presenting the shallow position information extraction module (SPIEM) and the context interaction module (CIM) to extract positional and saliency information from the low-level feature maps, thereby maximizing the enhancement and retention of location information; (3) conducting extensive experiments and achieving promising performance gains on two large-scale object datasets. The ablation studies also verify the effectiveness of the core mechanisms in the LR-FPN for object detection in remote sensing. LR-FPN improves upon existing feature pyramid networks by addressing the shortcomings of neglecting low-level positional information and fine-grained context interaction. The SPIEM module extracts positional and saliency information from low-level feature maps to maintain accurate target location information. The CIM module enhances the object area by infusing robust location information into different layers of the original FPN through spatial and channel interaction. The LR-FPN is effective in remote sensing scenes where the network's ability to extract object location information and facilitate contextual interactions is crucial. The LR-FPN is evaluated on two large-scale remote sensing datasets, DOTAV1.0 and HRSC2016, and shows superior performance compared to state-of-the-art methods. The code and models will be publicly available.LR-FPN: Enhancing Remote Sensing Object Detection with Location Refined Feature Pyramid Network This paper proposes a novel Location Refined Feature Pyramid Network (LR-FPN) to enhance shallow positional information extraction 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 Context Interaction Module (CIM). SPIEM extracts positional and saliency information from low-level feature maps to maintain accurate target location information. CIM infuses robust location information into different layers of the original FPN through spatial and channel interaction, enhancing the object area. 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 (DOTAV1.0 and HRSC2016) demonstrate that the proposed LR-FPN is superior to state-of-the-art object detection approaches. The code and models will be publicly available. The main contributions of this paper are: (1) presenting a plug-and-play location refined feature pyramid network (LR-FPN) to enhance the extraction of shallow positional information and facilitate fine-grained context interaction; (2) presenting the shallow position information extraction module (SPIEM) and the context interaction module (CIM) to extract positional and saliency information from the low-level feature maps, thereby maximizing the enhancement and retention of location information; (3) conducting extensive experiments and achieving promising performance gains on two large-scale object datasets. The ablation studies also verify the effectiveness of the core mechanisms in the LR-FPN for object detection in remote sensing. LR-FPN improves upon existing feature pyramid networks by addressing the shortcomings of neglecting low-level positional information and fine-grained context interaction. The SPIEM module extracts positional and saliency information from low-level feature maps to maintain accurate target location information. The CIM module enhances the object area by infusing robust location information into different layers of the original FPN through spatial and channel interaction. The LR-FPN is effective in remote sensing scenes where the network's ability to extract object location information and facilitate contextual interactions is crucial. The LR-FPN is evaluated on two large-scale remote sensing datasets, DOTAV1.0 and HRSC2016, and shows superior performance compared to state-of-the-art methods. The code and models will be publicly available.