Accurate and reliable optical remote sensing image-based small-ship detection is crucial for maritime surveillance systems, but existing methods often struggle with balancing detection performance and computational complexity. In this paper, we propose a novel lightweight framework called \textit{HSI-ShipDetectionNet} that is based on high-order spatial interactions and is suitable for deployment on resource-limited platforms, such as satellites and unmanned aerial vehicles. HSI-ShipDetectionNet includes a prediction branch specifically for tiny ships and a lightweight hybrid attention block for reduced complexity. Additionally, the use of a high-order spatial interactions module improves advanced feature understanding and modeling ability. Our model is evaluated using the public Kaggle marine ship detection dataset and compared with multiple state-of-the-art models including small object detection models, lightweight detection models, and ship detection models. The results show that HSI-ShipDetectionNet outperforms the other models in terms of recall, and mean average precision (mAP) while being lightweight and suitable for deployment on resource-limited platforms.

本文提出一种基于光学遥感图像的小船检测轻量级框架HSI-ShipDetectionNet，通过高阶空间交互和轻量级混合注意力模块，实现了对资源受限平台（如卫星和无人机）的部署，模型在公共Kaggle海洋船只检测数据集上的表现优于其他小物体检测模型、轻量级检测模型和船只检测模型。

基于光学遥感图像的小型船只检测的高阶空间交互增强轻量级模型