Image recognition is vital for intelligent ships’ autonomous navigation. However, traditional methods often fail to accurately identify maritime objects’ spatial positions, especially under electromagnetic silence. We introduce the StereoYOLO method, an enhanced stereo vision-based object recognition and localization approach that serves autonomous vessels using only image sensors. It is specifically refined for maritime object recognition and localization scenarios through the integration of convolutional and coordinated attention modules. The method uses stereo cameras to identify and locate maritime objects in images and calculate their relative positions using stereo vision algorithms. Experimental results indicate that the StereoYOLO algorithm boosts the mean Average Precision at IoU threshold of 0.5 (mAP50) in object recognition by 5.23%. Furthermore, the variation in range measurement due to target angle changes is reduced by 6.12%. Additionally, upon measuring the distance to targets at varying ranges, the algorithm achieves an average positioning error of 5.73%, meeting the accuracy and robustness criteria for maritime object collision avoidance on experimental platform ships.

In addressing the challenges of trajectory prediction in multi-ship interaction scenarios and aiming to improve the accuracy of multi-ship trajectory prediction, this paper proposes a multi-ship trajectory prediction model, GL-STGCNN. The GL-STGCNN model employs a ship interaction adjacency matrix extraction module to obtain a more reasonable ship interaction adjacency matrix. Additionally, after obtaining the distribution of predicted trajectories using the model, a model predictive control trajectory correction method is introduced to enhance the accuracy and reasonability of the predicted trajectories. Through quantitative analysis of different datasets, it was observed that GL-STGCNN outperforms previous prediction models with a 31.8% improvement in the average displacement error metric and a 16.8% improvement in the final displacement error metric. Furthermore, trajectory correction through model predictive control shows a performance boost of 44.5% based on the initial predicted trajectory distribution. While GL-STGCNN excels in multi-ship interaction trajectory prediction by reasonably modeling ship interaction adjacency matrices and employing trajectory correction, its performance may vary in different datasets and ship motion patterns. Future work could focus on adapting the model’s ship interaction adjacency matrix modeling to diverse environmental scenarios for enhanced performance.