  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.
