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Artificial Intelligence Technology and Application in Marine Science and Engineering
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Artificial Intelligence Technology and Application in Marine Science and Engineering

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: 5 June 2026 | Viewed by 4645

Special Issue Editors

Dr. Miguel Ângelo Lellis Moreira

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Guest Editor
Operations Research Department, Fluminense Federal University (UFF), Niterói 24020-007, Brazil
Interests: application of operational research, machine learning, and artificial intelligence to support decision-making in complex environments; working with multicriteria models, dimensionality-reduction techniques, data-driven frameworks, and hybrid analytical approaches for strategic analysis; particularly interested in applying these methods to marine and naval problems, such as vessel performance analysis, naval systems operation, ocean monitoring, and risk assessment in maritime environments; exploring AI-based solutions for navigation, maritime safety, naval logistics, and strategic planning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marcos Dos Santos

E-Mail Website
Guest Editor
Operations Research Department, Naval College, Rio de Janeiro 20021-010, Brazil
Interests: involve artificial intelligence, machine learning, and operational research applied to strategic decision-support systems; focusing on developing and implementing multicriteria decision models, predictive algorithms, and uncertainty-handling techniques tailored to real-world problems; within the marine domain, issues related to naval sensor analysis, maritime traffic monitoring, ocean operations safety, and the assessment of technological vulnerabilities in maritime systems; interested in the use of oceanographic data, detection systems, risk modeling, and resource optimization to enhance marine-engineering processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in Artificial Intelligence (AI) have presented unprecedented opportunities for innovation in marine science and engineering. The integration of AI-driven techniques—including machine learning, deep learning, data mining, and intelligent optimization—has enabled breakthroughs in ocean observation, marine environment monitoring, resource management, and autonomous maritime systems.

This Special Issue aims to provide a multidisciplinary platform for researchers and engineers to present cutting-edge research and applications of AI technologies in marine contexts. Topics of interest include theoretical developments, computational frameworks, experimental studies, and case-based analyses that demonstrate the transformative role of AI in addressing complex marine and coastal challenges.

We welcome contributions that explore AI-based methodologies for data assimilation, predictive modeling, marine robotics, operational research decision-making, and sustainable blue-economy strategies. Both original research papers and comprehensive reviews are encouraged.

Dr. Miguel Ângelo Lellis Moreira
Prof. Dr. Marcos dos Santos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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 semimonthly 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

  • artificial intelligence
  • machine learning
  • deep learning
  • ocean data analytics
  • maritime operations optimization
  • environmental monitoring
  • ocean engineering
  • data-driven decision support
  • sustainable blue economy
  • operational research

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

51 pages, 3316 KB  
Article
Improving Quay Crane Productivity and Delay Management in Conventional Container Terminals Using Artificial Intelligence Tools
by George-Cosmin Partene, Florin Nicolae, Florin Postolache and Sorin Ionescu
J. Mar. Sci. Eng. 2026, 14(8), 749; https://doi.org/10.3390/jmse14080749 - 19 Apr 2026
Viewed by 408
Abstract
This study proposes an integrated artificial intelligence-based framework for modeling and predicting quay crane productivity and operational delays in conventional container terminals, addressing key limitations in the existing port analytics literature. The research introduces a novel dual-mode machine learning architecture that explicitly separates [...] Read more.
This study proposes an integrated artificial intelligence-based framework for modeling and predicting quay crane productivity and operational delays in conventional container terminals, addressing key limitations in the existing port analytics literature. The research introduces a novel dual-mode machine learning architecture that explicitly separates retrospective prediction (forecast mode) from pre-operational decision support (decision mode), addressing a critical gap in existing literature where predictive models are rarely aligned with real-world informational constraints. The framework is applied to a high-resolution, real-world dataset comprising ship-level operations over a three-year period (2023–2025), incorporating a structured representation of 27 delay types and multiple resource allocation variables. A multi-indicator modeling strategy is employed, simultaneously analyzing four productivity metrics (RQCP, GMPH, WBMPH and NMPH), thus allowing for a systematic comparison of their structural sensitivities to delays, congestion, and equipment utilization. The results reveal a clear hierarchy of predictability and operational behavior: structurally driven indicators such as RQCP and GMPH exhibit high predictive stability, while delay-sensitive indicators such as NMPH display greater variability, reflecting real-time operational disruptions. The consistent model performance in forecasting and decision-making indicates significant predictive value in pre-operational variables, endorsing its utility for uncertain decision-making. Sensitivity analysis reveals a critical nonlinear congestion threshold affecting predictive accuracy under extreme operational strain. Employing a combination of multi-indicator productivity modeling, structured delay classification, and ensemble learning within an integrated analytical framework, this research enhances both methodological and practical insights into port operations, aiding in merging predictive analytics with operational decision-making in container terminals to enhance resource allocation, delay handling, and container terminal efficiency. Full article
(This article belongs to the Special Issue Artificial Intelligence Technology and Application in Marine Science and Engineering)
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20 pages, 2662 KB  
Article
A Synthetic Data-Driven Approach for Oil Spill Detection: Fine-Tuning YOLOv11-Seg with LIC-Based Ocean Flow Modeling
by Farkhod Akhmedov, Khujakulov Toshtemir Abdikhafizovich, Furkat Bolikulov and Fazliddin Makhmudov
J. Mar. Sci. Eng. 2026, 14(7), 608; https://doi.org/10.3390/jmse14070608 - 26 Mar 2026
Viewed by 525
Abstract
Oil spills represent a severe environmental hazard, threatening marine and coastal ecosystems, biodiversity, and socio-economic stability. Timely and accurate detection of such incidents is critical for mitigating their ecological and economic consequences. Conventional detection techniques, including manual inspection and satellite-based observation, remain limited [...] Read more.
Oil spills represent a severe environmental hazard, threatening marine and coastal ecosystems, biodiversity, and socio-economic stability. Timely and accurate detection of such incidents is critical for mitigating their ecological and economic consequences. Conventional detection techniques, including manual inspection and satellite-based observation, remain limited by high operational costs, temporal delays, and restricted spatial coverage. To overcome these limitations, this study introduces a comprehensive computer vision framework that addresses two core challenges: (i) the construction of a large-scale, high-quality synthetic oil spill dataset through mask extraction and seamless blending of oil spill regions with diverse oceanic backgrounds, and (ii) the development of a fine-tuned YOLOv11m-seg detection model trained on this enriched dataset. To further enhance the realism and spatial distinctiveness of oil spill textures, the Line Integral Convolution (LIC) is applied to estimate and visualize ocean surface flow patterns, generating coherent streamline textures that simulate the natural diffusion and transport of oil in water. The model exhibited strong generalization and precision, achieving a training accuracy exceeding IoU@0.50-0.95 to 85% over 50 epochs. Evaluation metrics confirmed its reliability, with an F1 score of 94%, precision of 94%, and recall (mAP@0.50) of 94%. These results demonstrate that the developed approach not only enhances dataset diversity but also substantially improves the accuracy and representativeness of real-time oil spill detection in marine environments. Full article
(This article belongs to the Special Issue Artificial Intelligence Technology and Application in Marine Science and Engineering)
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28 pages, 2691 KB  
Article
Effectiveness of Attention Mechanisms in YOLOv8 for Maritime Vessel Detection
by Changui Lee and Seojeong Lee
J. Mar. Sci. Eng. 2026, 14(5), 433; https://doi.org/10.3390/jmse14050433 - 26 Feb 2026
Viewed by 649
Abstract
Maritime vessel detection in nearshore waters is a fundamental capability for artificial intelligence (AI)-enabled maritime transportation systems, including coastal monitoring, traffic management, and digital maritime services. Although attention mechanisms are widely incorporated into YOLO-based detectors, their relative effectiveness in marine environments under strictly [...] Read more.
Maritime vessel detection in nearshore waters is a fundamental capability for artificial intelligence (AI)-enabled maritime transportation systems, including coastal monitoring, traffic management, and digital maritime services. Although attention mechanisms are widely incorporated into YOLO-based detectors, their relative effectiveness in marine environments under strictly controlled experimental conditions remains insufficiently clarified. This study presents a systematic comparison of Coordinate Attention (CA), Convolutional Block Attention Module (CBAM), and CLIP-based semantic fusion within a unified YOLOv8n framework for binary discrimination between ships and fishing boats in cluttered coastal imagery. All model variants were trained under identical data partitions and optimization settings to isolate architectural effects. The experimental results show that CA achieves the highest localization robustness (mAP@0.5:0.95 = 0.6127) and substantially improves precision (+7.13% over baseline), while CBAM provides the most balanced performance with the highest F1-score. In contrast, CLIP-based semantic fusion consistently degrades detection reliability, indicating limitations of global vision–language representations in small-scale maritime datasets. Precision–Recall and F1 analyses further reveal architecture-specific confidence calibration behaviors relevant to deployment-sensitive maritime applications. The findings provide practical guidance for selecting attention mechanisms in AI-driven maritime perception systems and support reliable AI integration in marine science and engineering applications. Full article
(This article belongs to the Special Issue Artificial Intelligence Technology and Application in Marine Science and Engineering)
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31 pages, 4976 KB  
Article
CEH-DETR: A State Space-Based Framework for Efficient Multi-Scale Ship Detection
by Xiaolin Zhang, Ru Wang and Shengzheng Wang
J. Mar. Sci. Eng. 2026, 14(3), 279; https://doi.org/10.3390/jmse14030279 - 29 Jan 2026
Viewed by 485
Abstract
Ship detection in optical images is critical for maritime supervision but faces challenges from scale variations and complex backgrounds. Existing detectors often struggle to balance global context modeling with computational efficiency. To address this, we propose Contextual Efficient Hierarchical DETR (CEH-DETR), an efficient [...] Read more.
Ship detection in optical images is critical for maritime supervision but faces challenges from scale variations and complex backgrounds. Existing detectors often struggle to balance global context modeling with computational efficiency. To address this, we propose Contextual Efficient Hierarchical DETR (CEH-DETR), an efficient framework for multi-scale ship detection. First, we introduce the Cross-stage Parallel State Space Hidden Mixer (CPSHM) backbone, integrating State Space Models with CNNs to capture global dependencies with linear complexity. Second, the Efficient Adaptive Feature Integration (EAFI) module reduces attention complexity to linear using Token Statistics-based Attention. Third, the Hierarchical Attention-guided Feature Pyramid Network (HAFPN) effectively fuses multi-scale features while preserving spatial details. Experiments on the ABOships dataset demonstrate that CEH-DETR achieves a superior balance between accuracy and efficiency. Relative to the baseline RT-DETR, our approach achieves a parameter reduction of 25.6% while increasing mAP@50 by 2.0 percentage points and boosting inference speed to 133.7 FPS (+112.1%), making it highly suitable for real-time maritime surveillance. Full article
(This article belongs to the Special Issue Artificial Intelligence Technology and Application in Marine Science and Engineering)
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19 pages, 2688 KB  
Article
Framework for the Development of a Process Digital Twin in Shipbuilding: A Case Study in a Robotized Minor Pre-Assembly Workstation
by Ángel Sánchez-Fernández, Elena-Denisa Vlad-Voinea, Javier Pernas-Álvarez, Diego Crespo-Pereira, Belén Sañudo-Costoya and Adolfo Lamas-Rodríguez
J. Mar. Sci. Eng. 2026, 14(1), 106; https://doi.org/10.3390/jmse14010106 - 5 Jan 2026
Viewed by 1136
Abstract
This article proposes a framework for the development of process digital twins (DTs) in the shipbuilding sector, based on the ISO 23247 standard and structured around the achievement of three levels of digital maturity. The framework is demonstrated through a real pilot cell [...] Read more.
This article proposes a framework for the development of process digital twins (DTs) in the shipbuilding sector, based on the ISO 23247 standard and structured around the achievement of three levels of digital maturity. The framework is demonstrated through a real pilot cell developed at the Innovation and Robotics Center of NAVANTIA—Ferrol shipyard, incorporating various cutting-edge technologies such as robotics, artificial intelligence, automated welding, computer vision, visual inspection, and autonomous vehicles for the manufacturing of minor pre-assembly components. Additionally, the study highlights the crucial role of discrete event simulation (DES) in adapting traditional methodologies to meet the requirements of Process digital twins. By addressing these challenges, the research contributes to bridging the gap in the current state of the art regarding the development and implementation of Process digital twins in the naval sector. Full article
(This article belongs to the Special Issue Artificial Intelligence Technology and Application in Marine Science and Engineering)
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25 pages, 3099 KB  
Article
Research on Improved PPO-Based Unmanned Surface Vehicle Trajectory Tracking Control Integrated with Pure Pursuit Guidance
by Hongyu Li, Runyu Yang, Yu Zhang, Yicheng Wen, Qunhong Tian, Weizhuang Ma, Zongsheng Wang and Shaobo Yang
J. Mar. Sci. Eng. 2026, 14(1), 70; https://doi.org/10.3390/jmse14010070 - 30 Dec 2025
Viewed by 734
Abstract
To address the low trajectory tracking accuracy and limited robustness of conventional reinforcement learning algorithms under complex marine environments involving wind, wave, and current disturbances, this study proposes a proximal policy optimization (PPO) algorithm incorporating an intrinsic curiosity mechanism to solve the unmanned [...] Read more.
To address the low trajectory tracking accuracy and limited robustness of conventional reinforcement learning algorithms under complex marine environments involving wind, wave, and current disturbances, this study proposes a proximal policy optimization (PPO) algorithm incorporating an intrinsic curiosity mechanism to solve the unmanned surface vehicle (USV) trajectory tracking control problem. The proposed approach is developed on the basis of a three-degree-of-freedom (3-DOF) USV model and formulated within a Markov decision process (MDP) framework, where a multidimensional state space and a continuous action space are defined, and a multi-objective composite reward function is designed. By incorporating a pure pursuit guidance algorithm, the complexity of engineering implementation is reduced. Furthermore, an improved PPO algorithm integrated with an intrinsic curiosity mechanism is adopted as the trajectory tracking controller, in which the exploration incentives provided by the intrinsic curiosity module (ICM) guide the agent to explore the state space efficiently and converge rapidly to an optimal control policy. The final experimental results indicate that, compared with the conventional PPO algorithm, the improved PPO–ICM controller achieves a reduction of 54.2% in average lateral error and 47.1% in average heading error under simple trajectory conditions. Under the complex trajectory condition, the average lateral error and average heading error are reduced by 91.8% and 41.9%, respectively. These results effectively demonstrate that the proposed PPO–ICM algorithm attains high tracking accuracy and strong generalization capability across different trajectory scenarios, and can provide a valuable reference for the application of intelligent control algorithms in the USV domain. Full article
(This article belongs to the Special Issue Artificial Intelligence Technology and Application in Marine Science and Engineering)
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