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Advanced Research on the Sustainable Maritime Transportation (2nd Edition)

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A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 15970

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Special Issue Editors

Prof. Dr. Xianhua Wu

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Guest Editor

School of Economics and Management, Shanghai Maritime University, Shanghai, China
Interests: maritime disaster risk analysis; big data analysis; applied statistics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jian Wu

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Guest Editor

School of Economics and Management, Shanghai Maritime University, Shanghai, China
Interests: decision sciences; shipping assessment; big data analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Lang Xu

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Guest Editor

College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China
Interests: port and shipping governance; sustainable transportation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a platform where researchers can share their latest findings and insights regarding sustainable maritime transportation. This Special Issue covers a wide range of topics related to sustainable maritime transportation, including the following:

Green shipping technologies: It explores the latest developments in green shipping technologies, such as alternative fuels, energy-efficient propulsion systems, and emission reduction technologies.
Maritime safety: It refers to the measures and practices that are put in place to prevent accidents and incidents at sea. One of the key areas is the use of autonomous vessels. Another area includes terrorist attacks (such as the Red Sea event).
Sustainable port operations: This Special Issue also covers sustainable port operations, including the use of renewable energy sources, waste management, and sustainable logistics.
Environmental impact assessment: This Special Issue includes studies concerning the environmental impact of maritime transportation, such as the impact of shipping on marine ecosystems and the effects of climate change on shipping.
The evaluation of sustainable development: For example, the reduction capacity performance evaluation of intermodal transport emission and feasibility studies of green and low-carbon technologies for ships, ports, or maritime transportation.
Maritime Transportation: this Special Issue also includes studies concerning industrial development and sustainable environment relating to maritime transportation.

In light of the success of this Special Issue and the pressing issue, we would like to announce the 2nd Edition.

Prof. Dr. Xianhua Wu
Prof. Dr. Jian Wu
Dr. Lang Xu
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

sustainable maritime transportation
green shipping
energy efficiency
alternative fuels
emission reduction
maritime safety
maritime accident
port
environmental impact
feasibility study

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Published Papers (13 papers)

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Research

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24 pages, 3497 KiB

Open AccessArticle

An Innovation Machine Learning Approach for Ship Fuel-Consumption Prediction Under Climate-Change Scenarios and IMO Standards

by Bassam M. Aljahdali, Yazeed Alsubhi, Ayman F. Alghanmi, Hussain T. Sulaimani and Ahmad E. Samman

J. Mar. Sci. Eng. 2025, 13(4), 805; https://doi.org/10.3390/jmse13040805 - 17 Apr 2025

Viewed by 395

Abstract

This study introduces an innovative Emotional Artificial Neural Network (EANN) model to predict ship fuel consumption with high accuracy, addressing the challenges posed by complex environmental conditions and operational variability. This research examines the impact of climate change on maritime operations and fuel efficiency by analyzing climatic variables such as wave period, wind speed, and sea-level rise. The model’s performance is assessed using two ship types (bulk carrier and container ship with max 60,000 dead weight tonnage (DWT)) under various climate scenarios. A comparative analysis demonstrates that the EANN model significantly outperforms the conventional Feedforward Neural Network (FFNN) in predictive accuracy. For bulk carriers, the EANN achieved a Root Mean Squared Error (RMSE) of 5.71 tons/day during testing, compared to 9.91 tons/day for the FFNN model. Similarly, for container ships, the EANN model achieved an RMSE of 5.97 tons/day, significantly better than the FFNN model’s 10.18 tons/day. A sensitivity analysis identified vessel speed as the most critical factor, contributing 33% to the variance in fuel consumption, followed by engine power and current speed. Climate-change simulations showed that fuel consumption increases by an average of 22% for bulk carriers and 19% for container ships, highlighting the importance of operational optimizations. This study emphasizes the efficacy of the EANN model in predicting fuel consumption and optimizing ship performance. The proposed model provides a framework for improving energy efficiency and supporting compliance with International Maritime Organization Standards (IMO) environmental standards. Meanwhile, the Carbon Intensity Indicator (CII) evaluation results emphasize the urgent need for measures to reduce carbon emissions to meet the IMO’s 2030 standards. Full article

(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))

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24 pages, 4192 KiB

Open AccessArticle

Comparative Assessment of the Thermal Load of a Marine Engine Operating on Alternative Fuels

by Sergejus Lebedevas and Edmonas Milašius

J. Mar. Sci. Eng. 2025, 13(4), 748; https://doi.org/10.3390/jmse13040748 - 8 Apr 2025

Viewed by 299

Abstract

The decarbonization of the operational fleet through the implementation of renewable and low-carbon fuels (LCFs) is considered a key factor in achieving the regulatory greenhouse gas (GHG) reduction targets set by the IMO and the EU. In parallel with optimizing engine energy efficiency and emission characteristics during retrofitting for LCF operations, it is equally important to assess and ensure the reliability of engine components under permissible thermal and mechanical loads. This study investigated the key factors influencing thermal and mechanical stresses on the cylinder–piston assembly components as the engine’s operation shifts from diesel to biodiesel, natural gas, methanol, or ammonia. The methodological foundation of this research was an original comparative analysis method that evaluates the impacts of thermal stress and combustion cycle energy efficiency factors. The combustion cycle energy parameters were modeled using a single-zone mathematical model. The thermal load factor was determined based on the ALPHA (α_gas) coefficient of heat transfer intensity and the average combustion gas temperature (T_avg). The optimization of the combustion cycle during retrofitting was simulated without changes to the engine structure (or without “major” modernization, according to IMO terminology), with modifications limited to the engine’s combustion adjustment parameters. A key characteristic of the transition from diesel to LCFs is a significant increase in the maximum cycle pressure (Pmax), a factor influencing mechanical stresses: ammonia, +43%; LNG, +28%; methanol, +54–70%; biodiesel, no significant changes. This study confirms the adopted strategy to maintain thermal load factors for engine components equal to Dmax conditions. It is emphasized that, after ensuring Pmax-idem conditions, the thermal load during LCF operation aligns closely with the characteristic diesel level with minimal deviation. The thermal load reduction is associated with an increase in the excess air coefficient (λ) and a controlled reduction in the compression ratio within an allowable variation of ±1 unit. Based on statistical correlations, a rational increase in λ was identified, reaching up to 2.5 units. Considering the real-world operational load cycle structure of marine engines, further research will focus on analyzing thermal and mechanical stresses according to ISO 81/78, as well as E2 and E3 operational cycles. Full article

(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))

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34 pages, 7859 KiB

Open AccessArticle

Container Liner Shipping System Design Considering Methanol-Powered Vessels

by Zhaokun Li, Xinke Yu, Jianning Shang, Kang Chen, Xu Xin, Wei Zhang and Shaoqiang Yu

J. Mar. Sci. Eng. 2025, 13(4), 709; https://doi.org/10.3390/jmse13040709 - 2 Apr 2025

Viewed by 290

Abstract

The transition from the use of heavy fuel oil (HFO) to the use of green fuels (e.g., methanol) for container liner shipping presents a significant challenge for liner shipping system design (LSSD) in terms of achieving emission reductions. While methanol, including both green and gray methanol, offers environmental benefits, its lower energy density introduces operational complexities. Motivated by the aforementioned background, we establish a bi-level programming model. This model integrates liner speed management and bunker fuel management strategies (i.e., bunkering port selection and bunkering amount determination) with traditional network design decision (i.e., fleet deployment, shipping network design, and slot allocation) optimization. Specifically, the upper-level model optimizes the number of liners deployed in the fleet and shipping network structure, whereas the lower-level model coordinates decisions associated with liner sailing speed management, bunker fuel management, and slot allocation. Moreover, we propose an adaptive piecewise linearization approach combined with a genetic algorithm, which can efficiently solve large-scale instances. Sensitivity analyses of fuel types and fuel prices are conducted to demonstrate the effectiveness of the model and algorithm. Overall, our paper offers valuable insights for policymakers in designing customized emission reduction policies to support the green fuel transition in the maritime industry. Full article

(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))

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30 pages, 7457 KiB

Open AccessArticle

Improving Green Shipping by Using Alternative Fuels in Ship Diesel Engines

by Sergii Sagin, Oleksandr Haichenia, Sergey Karianskyi, Oleksiy Kuropyatnyk, Roman Razinkin, Arsenii Sagin and Oleksandr Volkov

J. Mar. Sci. Eng. 2025, 13(3), 589; https://doi.org/10.3390/jmse13030589 - 17 Mar 2025

Cited by 3 | Viewed by 629

Abstract

This paper aims to consider the issue of increasing the environmental friendliness of shipping by using alternative fuels in marine diesel engines. It has been determined that marine diesel engines are not only the main heat engines used on ships of sea and inland waterway transport, but are also sources of emissions of toxic components with exhaust gases. The main compounds whose emissions are controlled and regulated by international organizations are sulfur oxides (SO_X) and nitrogen oxides (NO_X), as well as carbon dioxide (CO₂). Reducing NO_X and CO₂ emissions while simultaneously increasing the environmental friendliness of shipping is possible by using fuel mixtures in marine diesel engines that include biodiesel fuel. During the research carried out on Wartsila 6L32 marine diesel engines (Shanghai Wartsila Qiyao Diesel Co. Ltd., Shanghai, China), RMG500 and DMA10 petroleum fuels were used, as well as their mixtures with biodiesel fuel FAME. It was found that when using mixtures containing 10–30% of FAME biodiesel, NO_X emissions are reduced by 11.20–27.10%; under the same conditions, CO₂ emissions are reduced by 5.31–19.47%. The use of alternative fuels in marine diesel engines (one of which is biodiesel and fuel mixtures containing it) is one of the ways to increase the level of environmental sustainability of seagoing vessels and promote ecological shipping. This is of particular relevance when operating vessels in special ecological areas of the World Ocean. The relatively low energy intensity of the method of creating and using such fuel mixtures contributes to the spread of its use on many means of maritime transport. Full article

(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))

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17 pages, 2115 KiB

Open AccessArticle

Expanding IMO Compendium with NAVTEX Messages for Maritime Single Window

by Changui Lee and Seojeong Lee

J. Mar. Sci. Eng. 2024, 12(12), 2328; https://doi.org/10.3390/jmse12122328 - 19 Dec 2024

Viewed by 889

Abstract

The International Maritime Organization (IMO) introduced the Maritime Service Portfolio (MSP) and Maritime Single Window (MSW) to enhance the digitalization and efficiency of maritime transportation. While the MSP defines 16 maritime services focused on safety, security, efficiency, and environmental protection, the MSW provides a unified digital platform for submitting and processing information related to a ship’s operations. To support the implementation of MSW, the IMO Compendium provides standardized data sets and reference models to enable seamless information exchange across maritime systems. This paper proposes an expansion of the IMO Compendium to integrate the MSP’s maritime safety information service into the MSW environment. The study focuses on the integration of NAVTEX messages, a key source of navigational safety information, by identifying their key attributes and structuring them according to the IHO S-124 standard. A case study demonstrates the feasibility of the proposed data structure by transforming a sample NAVTEX message into the expanded IMO Compendium format and testing its transmission using an open-source MQTT library. This paper provides a structured methodology for integrating NAVTEX messages, effectively bridging legacy systems with modern digital infrastructures and facilitating enhanced interoperability in maritime operations. The proposed data structure will be presented to standardization bodies for further consideration, contributing to ongoing efforts to improve maritime operational efficiency and support digital transformation. Full article

(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))

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16 pages, 4180 KiB

Open AccessArticle

Integrating Bayesian Network and Cloud Model to Probabilistic Risk Assessment of Maritime Collision Accidents in China’s Coastal Port Waters

by Zhuang Li, Xiaoming Zhu, Shiguan Liao, Jianchuan Yin, Kaixian Gao and Xinliang Liu

J. Mar. Sci. Eng. 2024, 12(12), 2113; https://doi.org/10.3390/jmse12122113 - 21 Nov 2024

Viewed by 1034

Abstract

Ship collision accidents have a greatly adverse impact on the development of the shipping industry. Due to the uncertainty relating to these accidents, maritime risk is often difficult to accurately quantify. This study innovatively proposes a comprehensive method combining qualitative and quantitative methods to predict the risk of ship collision accidents. First, in view of the uncertain impact of risk factors, the Bayesian network analysis method was used to characterize the correlations between risk factors, and a collision accident risk assessment network model was established. Secondly, in view of the uncertainty relating to the information about risk factors, a subjective data quantification method based on the cloud model was adopted, and the quantitative reasoning of collision accident risk was determined based on multi-source data fusion. The proposed method was applied to the spatiotemporal analysis of ship collision accident risk in China’s coastal port waters. The results show that there is a higher risk of collision accidents in Guangzhou Port and Ningbo Port in China, the potential for ship collision accidents in southern China is greater, and the occurrence of ship collision accidents is most affected by the environment and operations of operators. Combining the Bayesian network and cloud model and integrating multi-source data information to conduct an accident risk assessment, this innovative analysis method has significance for improving the prevention of and response to risks of ship navigation operations in China’s coastal ports. Full article

(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))

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16 pages, 2184 KiB

Open AccessArticle

Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments

by Qifeng Yu, Yudong Han, Xinjia Gao, Wuguang Lin and Yi Han

J. Mar. Sci. Eng. 2024, 12(10), 1754; https://doi.org/10.3390/jmse12101754 - 4 Oct 2024

Cited by 3 | Viewed by 2335

Abstract

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. Full article

(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))

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18 pages, 3731 KiB

Open AccessArticle

Algorithm Design for an Online Berth Allocation Problem

by Cong Chen, Fanxin Wang, Jiayin Pan, Lang Xu and Hongming Gao

J. Mar. Sci. Eng. 2024, 12(10), 1722; https://doi.org/10.3390/jmse12101722 - 30 Sep 2024

Viewed by 893

Abstract

In this paper, we investigate an online berth allocation problem, where vessels arrive one by one and their information is revealed upon arrival. Our objective is to design online algorithms to minimize the maximum load of all berths (makespan). We first demonstrate that the widely used Greedy algorithm has a very poor theoretical guarantee; specifically, the competitive ratio of the Greedy algorithm for this problem is lower bounded by

Ω (\log m / \log \log m)

, which increases with the number of berths

m

. On account of this, we borrow an idea from algorithms for the online strip packing problem and provide a comprehensive theoretical analysis of the Revised Shelf (RS) algorithm as applied to our berth allocation problem. We prove that the competitive ratio of RS for our problem is 5, improving on the original competitive ratio of 6.66 for the online strip packing problem. Through numerical studies, we examine the RS algorithm and Greedy algorithm in an average case. The numerical simulation of competitive ratios reveals distinct advantages for different algorithms depending on job size. For smaller job sizes, the Greedy algorithm emerges as the most efficient, while for medium-sized jobs, the RS algorithm proves to be the most effective. Full article

(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))

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24 pages, 3881 KiB

Open AccessArticle

Methodological Solutions for Predicting Energy Efficiency of Organic Rankine Cycle Waste Heat Recovery Systems Considering Technological Constraints

by Sergejus Lebedevas and Tomas Čepaitis

J. Mar. Sci. Eng. 2024, 12(8), 1303; https://doi.org/10.3390/jmse12081303 - 1 Aug 2024

Cited by 4 | Viewed by 1664

Abstract

Solving strategic IMO tasks for the decarbonization of maritime transport and the dynamics of its controlling indicators (EEDI, EEXI, CII) involves the comprehensive use of renewable and low-carbon fuels (LNG, biodiesel, methanol in the mid-term perspective of 2030, ammonia, and hydrogen to achieve zero emissions by 2050) and energy-saving technologies. The technology of regenerating secondary heat sources of the ship’s power plant WHR in the form of an Organic Rankine Cycle (ORC) is considered one of the most promising solutions. The attractiveness of the ORC is justified by the share of the energy potential of WHR at 45–50%, almost half of which are low-temperature WHR (80–90 °C and below). However, according to DNV GL, the widespread adoption of WHR-ORC technologies, especially on operating ships, is hindered by the statistical lack of system prototypes combined with the high cost of implementation. Developing methodological tools for justifying the energy efficiency indicators of WHR–ORC cycle implementation is relevant at all stages of design. The methodological solutions proposed in this article are focused on the initial stages of comparative evaluation of alternative structural solutions (without the need to use detailed technical data of the ship’s systems, power plant, and ORC nodes), expected indicators of energy efficiency, and cycle performance. The development is based on generalized results of variation studies of the ORC in the structure of the widely used main marine medium-speed diesel engine Wärtsilä 12V46F (14,400 kW, 500 min⁻¹) in the operational load cycle range of 25–100% of nominal power. The algorithm of the proposed solutions is based on the established interrelationship of the components of the ORC energy balance in the P-h diagram field of thermodynamic indicators of the cycle working fluid (R134a was used). The implemented strategy does allow, in graphical form, for justifying the choice of working fluid and evaluating the energy performance and efficiency of alternative WHR sources for the main engine, taking into account the design solutions of the power turbine and the technological constraints of the ORC condensation system. The verification of the developed methodological solutions is served by the results of comprehensive variation studies of the ORC performed by the authors using the professionally oriented thermoengineering tool “Thermoflow” and the specification data of Wärtsilä 12V46F with an achieved increase in energy efficiency indicators by 21.4–7%. Full article

(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))

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30 pages, 4155 KiB

Open AccessArticle

Thermo-Economic Comparison between Three Different Electrolysis Technologies Powered by a Conventional Organic Rankine Cycle for the Green Hydrogen Production Onboard Liquefied Natural Gas Carriers

by Doha Elrhoul, Manuel Naveiro and Manuel Romero Gómez

J. Mar. Sci. Eng. 2024, 12(8), 1287; https://doi.org/10.3390/jmse12081287 - 31 Jul 2024

Cited by 3 | Viewed by 2170

Abstract

The high demand for natural gas (NG) worldwide has led to an increase in the size of the LNG carrier fleet. However, the heat losses from this type of ship’s engines are not properly managed, nor is the excess boil-off gas (BOG) effectively utilised when generation exceeds the ship’s power demand, resulting in significant energy losses dissipated into the environment. This article suggests storing the lost energy into green H₂ for subsequent use. This work compares three different electrolysis technologies: solid oxide (SOEC), proton exchange membrane (PEME), and alkaline (AE). The energy required by the electrolysis processes is supplied by both the LNG’s excess BOG and engine waste heat through an organic Rankine cycle (ORC). The results show that the SOEC consumes (743.53 kW) less energy while producing more gH₂ (21.94 kg/h) compared to PEME (796.25 kW, 13.96 kg/h) and AE (797.69 kW, 10.74 kg/h). In addition, both the overall system and SOEC stack efficiencies are greater than those of PEME and AE, respectively. Although the investment cost required for AE (with and without H₂ compression consideration) is cheaper than SOEC and PEME in both scenarios, the cost of the H₂ produced by the SOEC is cheaper by more than 2 USD/kgH₂ compared to both other technologies. Full article

(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))

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17 pages, 6308 KiB

Open AccessArticle

Method of Plotting Fundamental Diagrams of Waterway Traffic Flow—Shipping-Lane Subdivision

by Siqing Zhuang, Yihua Liu and Zhiyuan Xu

J. Mar. Sci. Eng. 2024, 12(7), 1163; https://doi.org/10.3390/jmse12071163 - 10 Jul 2024

Viewed by 1137

Abstract

The difference between waterway traffic and road traffic in terms of lane lines leads to the direct application of the method of plotting the fundamental diagram of road traffic flow to waterway traffic, and it is difficult to reveal the mechanism of waterway traffic flow operations. This study proposes a shipping-lane-subdivision approach to tackle this problem. Additionally, it introduces a more suitable fundamental diagram-plotting method for waterway traffic based on the aforementioned method. The southern channel in the estuary of the Yangtze River was taken as the research water, and the fundamental diagram of traffic flow in this water was plotted to verify the similarities between the fundamental diagram of waterway traffic flow and the fundamental diagram of road traffic flow. Upon evaluating the plotted fundamental diagram, it was determined that the blockage density of the subdivided shipping lane is around 6.5 vessels per nautical mile. This method has significant potential for its application in the theory of waterway traffic flow. Full article

(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))

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19 pages, 8829 KiB

Open AccessArticle

Detection and Analysis of Corrosion on Coated Metal Surfaces Using Enhanced YOLO v5 Algorithm for Anti-Corrosion Performance Evaluation

by Qifeng Yu, Yudong Han, Wuguang Lin and Xinjia Gao

J. Mar. Sci. Eng. 2024, 12(7), 1090; https://doi.org/10.3390/jmse12071090 - 27 Jun 2024

Cited by 13 | Viewed by 2341

Abstract

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. Full article

(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))

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Review

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20 pages, 1609 KiB

Open AccessReview

Marine Intelligent Technology as a Strategic Tool for Sustainable Development: A Five-Year Systematic Analysis

by Qin Wang, Lang Xu and Jiyuan Wu

J. Mar. Sci. Eng. 2025, 13(5), 855; https://doi.org/10.3390/jmse13050855 - 25 Apr 2025

Viewed by 435

Abstract

Marine ecosystems are vital for maintaining biodiversity and ecological balance. However, these ecosystems face severe threats from habitat destruction, pollution, climate change, and overfishing. Addressing these challenges requires innovative solutions, including the adoption of marine intelligent technologies. This study examines the role of marine intelligent technologies in promoting ocean sustainability. By integrating bibliometric and trend analyses of 777 publications (2020–2024), the study identifies critical research directions and disparities in the application of these technologies across marine ecosystems, shipping, and fisheries. Key findings reveal that marine intelligent technologies have transformative potential, enabling real-time marine environmental monitoring, enhancing port operations, and reducing the ecological footprints of fisheries. The study highlights the importance of collaborative efforts in policy formulation, technological advancement, and global cooperation to achieve the United Nations’ Sustainable Development Goal 14. Insights from this research provide feasible pathways for aligning technological innovation with the sustainable management of marine resources. Full article

(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))

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