This paper addresses a problem affecting small target ship detection in which network models have a low recognition rate caused by the insignificance of features in small target ships.

A fusion method based on the integration of image and motion features is proposed to enrich the feature representation of small ships in scenarios where the features of small target ship images are not prominent. Additionally, a hybrid attention model incorporates the prior information of ship targets under data-driven conditions to enhance the model's perception and utilization of key features.

The proposed method achieves the recognition of small target ships with a resolution of 720P at a distance of up to 4 kilometers, enabling wide-area ship recognition and localization functionality.

The improved target recognition network exhibits pixel-level small target detection capability while also demonstrating robustness against environmental noise interference, thereby overcoming the bottleneck of the low recognition rate of network models in small target ship detection.