This study addresses the severe corrosion issues in the coastal regions of southern China by proposing an improved YOLO v5-GOLD-NWD model. Utilizing corrosion data from the National Center for Materials Corrosion and Protection Science of China, a dataset was constructed for metal-surface corrosion under different protective coatings. This dataset was used for model training, testing, and comparison. Model accuracy was validated using precision, recall, F1 score, and prediction probability. The results demonstrate that the proposed improved model exhibits better identification precision in metal corrosion detection, achieving 78%, a 4% improvement compared to traditional YOLO v5 models. Additionally, through identification and statistical analysis of corrosion image datasets from five types of coated metal specimens, it was found that powder epoxy coating, fluorocarbon coating, epoxy coating, and chlorinated rubber coating showed good corrosion resistance after 24 months of exposure. Conversely, Wuxi anti-fouling coating exhibited poor corrosion resistance. After 60 months of natural exposure, the powder epoxy coating specimens had the highest corrosion occurrence probability, followed by chlorinated rubber coating and epoxy coating, with fluorocarbon coating showing relatively lower probability. The fluorocarbon coating demonstrated relatively good corrosion resistance at both 24 and 60 months of exposure. The findings of this study provide a theoretical basis for enhancing the corrosion protection effectiveness of steel structures in coastal areas.

Coastal areas face severe corrosion issues, posing significant risks and economic losses to equipment, personnel, and the environment. YOLO v5, known for its speed, accuracy, and ease of deployment, has been employed for the rapid detection and identification of marine corrosion. However, corrosion images often feature complex characteristics and high variability in detection targets, presenting significant challenges for YOLO v5 in recognizing and extracting corrosion features. To improve the detection performance of YOLO v5 for corrosion image features, this study investigates two enhanced models: EfficientViT-NWD-YOLO v5 and Gold-NWD-YOLO v5. These models specifically target improvements to the backbone and neck structures of YOLO v5, respectively. The performance of these models for corrosion detection is analyzed in comparison with both YOLO v5 and NWD-YOLO v5. The evaluation metrics including precision, recall, F1-score, Frames Per Second (FPS), pre-processing time, inference time, non-maximum suppression time (NMS), and confusion matrix were used to evaluate the detection performance. The results indicate that the Gold-NWD-YOLO v5 model shows significant improvements in precision, recall, F1-score, and accurate prediction probability. However, it also increases inference time and NMS time, and decreases FPS. This suggests that while the modified neck structure significantly enhances detection performance in corrosion images, it also increases computational overhead. On the other hand, the EfficientViT-NWD-YOLO v5 model shows slight improvements in precision, recall, F1-score, and accurate prediction probability. Notably, it significantly reduces inference and NMS time, and greatly improves FPS. This indicates that modifications to the backbone structure do not notably enhance corrosion detection performance but significantly improve detection speed. From the application perspective, YOLO v5 and NWD-YOLO v5 are suitable for routine corrosion detection applications. Gold-NWD-YOLO v5 is better suited for scenarios requiring high precision in corrosion detection, while EfficientViT-NWD-YOLO v5 is ideal for applications needing a balance between speed and accuracy. The findings can guide decision making for corrosion health monitoring for critical infrastructure in coastal areas.

Amid growing global concerns about climate change and its environmental impact, the maritime sector is under increasing pressure to reduce carbon emissions. This study presents a system dynamics model that predicts and simulates vessel carbon emissions, considering different scenarios such as the implementation of carbon levies and the use of alternative marine fuels. The research focuses on the Pacific route, a key international container route, as a practical case study to simulate ship emissions along the Shanghai-Los Angeles container route under various emission reduction measures. Through a comparative analysis of different policy combinations, the findings demonstrate the effectiveness of carbon taxation and the adoption of diverse fuels in reducing carbon dioxide (CO₂) emissions from ships. Furthermore, the combination of these policies proves to be more effective in reducing emissions than implementing them individually. These results provide valuable insights for policymakers, industry professionals, and researchers working towards achieving low-carbon transitions in the shipping sector.

The Yangtze River estuary in China is characterized by a complex maritime geographical environment and presents significant challenges to ship manoeuvring and control, thereby increasing the risk of ship collision accidents. Based on the 2013–2022 shipwreck investigation report published by Shanghai and Zhejiang Maritime Safety Administration, this paper analyses the primary factors responsible for ship collision accident severity in the Yangtze River estuary from four aspects, namely ship, environment, human and management. Utilizing accident severity as the dependent variable and 24 factors, including ship type, gross tonnage, wind speed, operational errors and so on, as independent variables, the study employed a stepwise regression approach to filter the variables. Subsequently, an ordered probit regression model was constructed based on the 10 most influential variables, followed by a marginal effect analysis. The findings indicate that a ship's gross tonnage, wind speed, ocean current speed, offshore distance and day/night conditions significantly influence the likelihood of different accident levels. Specifically, wind speed, offshore distance and ocean current speed have a negative impact on minor and general accidents while positively affecting major and severe accidents. Gross tonnage and daytime/nighttime have a positive impact on minor and general accidents but negatively impact major and severe accidents. Moreover, general accidents exhibit the most pronounced marginal effect for each explanatory variable. The findings can help the shipping authorities to identify the causes of ship collision accidents and take effective measures to reduce such accidents, thereby enhancing the safety of ship navigation in the area.