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 order to realize the unsupervised extraction and identification of fault features in power electronic circuits, we proposed a fault diagnosis method based on sparse autoencoder (SAE) and broad learning system (BLS). Firstly, the feature is extracted by the sparse autoencoder, and the fault samples and feature vectors are combined as the input of the broad learning system. The broad learning system is trained based on the error precision step update method, and the system is used to the fault type identification. The simulation results of the thyristor fault diagnosis of the three-phase bridge rectifier circuit show that the method is effective and has better performance than other traditional methods.

Because solar energy is intermittent and a ship's power-system load fluctuates and changes abruptly, in this work, the solar radiation parameters were adjusted according to the latitude and longitude of the ship and the change of the sea environment. An objective function was constructed that accounted for the cost and service life simultaneously to optimize the configuration of the marine diesel engine hybrid energy system. Finally, the improved artificial bee colony algorithm was used to optimize and obtain the optimal system configuration. The feasibility of the method was verified by ship navigation tests. This method exhibited better configuration performance optimization than the traditional methods.

This study proposes an integrated framework for multi-ship trajectory prediction and motion planning based on joint learning, which significantly enhances the overall performance in multi-ship interaction scenarios by combining the global information from trajectory prediction with the local optimization capabilities of motion planning. In the trajectory prediction task, the proposed MTSGCN model achieves an average performance improvement of 26.3% compared to the Rain model, while the joint multi-task learning strategy yields a 26.9% performance gain over single-task learning, demonstrating the effectiveness of the multi-ship interaction adjacency matrix extraction module. In the motion planning task, the MTSGCN model outperforms the TFT model with an average performance improvement of 13.2%, whereas the MTSGCN-T model without joint learning experiences an 11.2% performance degradation. Furthermore, the study reveals that ship speed and heading decisions are influenced by multiple factors, with the inertial effect of historical parameters being the most significant. Additionally, the feature distributions of different datasets have a substantial impact on model performance.