Video monitoring is an important means of ship traffic supervision. In practice, regulators often need to use an electronic chart platform to determine basic information concerning ships passing through a video feed. To enrich the information in the surveillance video and to effectively use multimodal maritime data, this paper proposes a novel ship multi-object tracking technology based on improved single shot multibox detector (SSD) and DeepSORT algorithms. In addition, a night contrast enhancement algorithm is used to enhance the ship identification performance in night scenes and a multimodal data fusion algorithm is used to incorporate the ship automatic identification system (AIS) information into the video display. The experimental results indicate that the ship information tracking accuracies in the day and night scenes are 78⋅2% and 70⋅4%, respectively. Our method can effectively help regulators to quickly obtain ship information from a video feed and improve the supervision of a waterway.

Maritime search and rescue (SAR) plays a very important role in emergency waterway traffic situations, which is supposed to trigger severe personal casualties and property loss in maritime traffic accidents. The study aims to exploit an optimal allocation strategy with limited SAR resources deployed at navigation-constrained coastal islands. The study formulates the problem of SAR resource allocation in coastal areas into a non-linear optimization model. We explore the optimal solution for the SAR resource allocation problem under constraints of different ship and aircraft base station settings with the help of an enhanced particle swarm optimization (EPSO) model. Experimental results suggest that the proposed EPSO model can reasonably allocate the maritime rescue resources with a large coverage area and low time cost. The particle swarm optimization and genetic algorithm are further implemented for the purpose of model performance comparison. The research findings can help maritime traffic regulation departments to make more reasonable decisions for establishing SAR base stations.

Ship fuel consumption plays a crucial role not only in understanding ships’ energy efficiency but also in gaining insights into their emissions. However, enhancing the accuracy of these predictions poses significant challenges due to data limitations and the methods employed. Due to these factors, such as data variability and equipment characteristics, ship fuel consumption exhibits certain fluctuations under specific conditions. Previous fuel consumption prediction methods primarily generate a single specific value, making it difficult to capture the volatility of and variability in fuel consumption. To overcome this limitation, this paper proposes a novel method that integrates Gaussian process prediction with quantile regression theory to perform interval predictions of ship fuel consumption, providing a range of possible outcomes. Through comparative analyses with traditional methods, the possibility of using the method is verified and its results are validated. The results indicate the following: (1) at a 95% confidence level, the proposed method achieves a prediction interval coverage probability of 0.98 and a prediction interval normalized average width of 0.123, which are significantly better than those of the existing backpropagation neural network (BPNN) and gradient boosting decision tree (GBDT) quantile regression models; (2) the prediction accuracy of the proposed method is 92% for point forecasts; and (3) the proposed method is applicable to main datasets, including both noon report and sensor datasets. These findings provide valuable insights into interval predictions of ship fuel consumption and highlight their potential applications in related fields, emphasizing the importance of accurate interval predictions in intelligent energy efficiency optimization.

The electrodeposition technique was used to fabricate graphene and Cr particle-reinforced Ni–Cr–graphene coatings. The Rietveld refinement was utilized to analyze the microstructure of Ni deposits in the coatings. The properties including micro-hardness and corrosion behaviors of the coatings were also tested. Results showed that the addition of graphene particles contributed to the dendrite like structure on the surface of the Ni–Cr–graphene coating. The crystallite size and [200] texture of the Ni deposits in the Ni–Cr–graphene coatings were significantly decreased by the graphene particles. The crystallite size of 149.8 nm in the Ni-25–Cr-0–graphene coating was reduced to 35 nm in the Ni-25–Cr-8–graphene coating due to the addition of 8 g/L graphene to the electrolyte. The microstructure evolution of the Ni–Cr–graphene coatings brought about an enhancement in micro-hardness and corrosion resistance of the coatings. The micro-hardness of the coatings was improved from 260.1 HV_0.2 of the pure Ni coating to 285.9 HV_0.2 of the Ni-25–Cr-0–graphene coating and continually to 461.8 HV_0.2 of the Ni-25–Cr-8–graphene coating. In corrosion solution (3.5 wt.% NaCl), the corrosion current (6.22 μA/cm²) of the Ni-25–Cr-0–graphene coating could be decreased by about an order of magnitude through the addition of graphene particles, which was 0.33 μA/cm² for the Ni-25–Cr-8–graphene coating.

Since the spread of the coronavirus disease 2019 (COVID-19) pandemic, the transportation of cargo by ship has been seriously impacted. In order to prevent and control maritime COVID-19 transmission, it is of great significance to track and predict ship sailing behavior. As the nodes of cargo ship transportation networks, ports of call can reflect the sailing behavior of the cargo ship. Accurate hierarchical division of ports of call can help to clarify the navigation law of ships with different ship types and scales. For typical cargo ships, ships with deadweight over 10,000 tonnages account for 95.77% of total deadweight, and 592,244 berthing ships’ records were mined from automatic identification system (AIS) from January to October 2020. Considering ship type and ship scale, port hierarchy classification models are constructed to divide these ports into three kinds of specialized ports, including bulk, container, and tanker ports. For all types of specialized ports (considering ship scale), port call probability for corresponding ship type is higher than other ships, positively correlated with the ship deadweight if port scale is bigger than ship scale, and negatively correlated with the ship deadweight if port scale is smaller than ship scale. Moreover, port call probability for its corresponding ship type is positively correlated with ship deadweight, while port call probability for other ship types is negatively correlated with ship deadweight. Results indicate that a specialized port hierarchical clustering algorithm can divide the hierarchical structure of typical cargo ship calling ports, and is an effective method to track the maritime transmission path of the COVID-19 pandemic.

The ice conditions in Arctic waters are complex and variable, requiring ships to dynamically adjust their routes to ensure safe and efficient navigation. Traditional dynamic path planning algorithms struggle to address the extensive variability of Arctic ice conditions. To tackle this issue, this paper improves the D* Lite algorithm by leveraging the gradual and convergent nature of Arctic ice condition changes. The original algorithm’s local update and path extraction rules are modified to prevent chain updates triggered by minor localized changes, thereby reducing the frequency of updates in non-critical areas. By simulating dynamic route planning for ships in Arctic waters during both the freezing and melting periods, the improved D* Lite algorithm was compared with the original D* Lite algorithm and a global update algorithm in terms of voyage distance, risk coefficient, planning time, and the number of node updates. The computational results demonstrate that the improved D* Lite algorithm achieves planning results very similar to those of the original D* Lite algorithm and the global update algorithm at the lowest update cost, significantly enhancing the safety and efficiency of dynamic route planning for ships in Arctic waters.