LR-FPN: Enhancing Remote Sensing Object Detection with Location Refined Feature Pyramid Network

TL;DR

This paper introduces LR-FPN, a novel feature pyramid network that enhances remote sensing object detection by better capturing low-level positional information and fine-grained context interaction, leading to improved detection accuracy.

Contribution

The paper proposes a location refined feature pyramid network with modules for extracting and injecting positional information, improving detection performance over existing FPNs.

Findings

LR-FPN outperforms state-of-the-art methods on remote sensing datasets.

The modules effectively enhance positional and contextual feature extraction.

Significant improvements in detection accuracy demonstrated on DOTAV1.0 and HRSC2016 datasets.

Abstract

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.