提出开放提示遥感目标检测框架，支持多模态提示与实时高精度检测。
OpenRSD: Towards Open-prompts for Object Detection in Remote Sensing Images
- 引入多模态提示与多任务检测头，平衡精度与速度
- 在7个数据集上平均精度比YOLO-World高8.7%，达20.8 FPS
- 适合需要实时、泛化强的遥感图像分析场景
遥感目标检测虽取得显著进展，但多数研究仍聚焦于封闭集检测，限制了跨数据集的泛化能力。开放词汇目标检测（OVD）通过文本提示与视觉特征的多模态关联提供解决方案。然而，现有遥感图像的OVD方法受限于小规模数据集，且未能应对遥感解读的独特挑战，如定向目标检测，以及在多样化场景下对高精度与实时性的双重需求。为此，我们提出OpenRSD——一种通用的开放提示遥感目标检测框架。该框架支持多模态提示，并集成多任务检测头以兼顾准确率与实时性。此外，设计了多阶段训练流程以增强模型泛化能力。在七个公开数据集上的评估表明，OpenRSD在定向与水平边界框检测中均表现优异，具备实时推理能力，适用于大规模遥感图像分析。相比YOLO-World，其平均精度提升8.7%，推理速度达20.8 FPS。代码与模型将公开。
原文摘要 · Abstract (English)
Remote sensing object detection has made significant progress, but most studies still focus on closed-set detection, limiting generalization across diverse datasets. Open-vocabulary object detection (OVD) provides a solution by leveraging multimodal associations between text prompts and visual features. However, existing OVD methods for remote sensing (RS) images are constrained by small-scale datasets and fail to address the unique challenges of remote sensing interpretation, include oriented object detection and the need for both high precision and real-time performance in diverse scenarios. To tackle these challenges, we propose OpenRSD, a universal open-prompt RS object detection framework. OpenRSD supports multimodal prompts and integrates multi-task detection heads to balance accuracy and real-time requirements. Additionally, we design a multi-stage training pipeline to enhance the generalization of model. Evaluated on seven public datasets, OpenRSD demonstrates superior performance in oriented and horizontal bounding box detection, with real-time inference capabilities suitable for large-scale RS image analysis. Compared to YOLO-World, OpenRSD exhibits an 8.7\% higher average precision and achieves an inference speed of 20.8 FPS. Codes and models will be released.
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