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AI Applications in the Maritime Sector
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AI Applications in the Maritime Sector

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2337

Special Issue Editors

Prof. Dr. Igor Vujović

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Guest Editor
Signal Processing, Analysis, and Advanced Diagnostics Research and Education Laboratory (SPAADREL), Department for Marine Electrical Engineering and Information Technologies, Faculty of Maritime Studies, University of Split, 21000 Split, Croatia
Interests: signal processing and analysis; biomedical signal processing; video surveillance; ANN
Special Issues, Collections and Topics in MDPI journals
Dr. Joško Šoda

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Guest Editor
Signal Processing, Analysis, and Advanced Diagnostics Research and Education Laboratory (SPAADREL), Faculty of Maritime Studies, University of Split, 21000 Split, Croatia
Interests: time–frequency analysis; biomedical signal processing; applied statistical signal processing; digital signal processing; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is currently experiencing increasing usage in multiple areas. The rapid progress in this field is affecting more and more people in almost all areas of human activity and work. One of the most sensitive areas for AI application (due to regulations) is the maritime sector. Maritime environments provide harsh conditions for the input of sensors for AI applications. It is important to explore the possibilities of AI in the maritime sector from many angles, ranging from scientific curiosity to commercial interest.

This Special Issue, ‘AI Applications in the Maritime Sector’, welcomes submissions on this promising application area. This call for papers is open to a wide range of scholarship addressing the latest applications of big data and AI in the service industry, covering technologies, drones, robotics, psychological and economic aspects, shipbuilding, advanced interfaces, and research trends to provide insights into ways to expand the use of AI in the maritime sector. This Special Issue welcomes contributions dealing with countermeasures for AI privacy protection and safeguards for AI deployments in military applications; it will also examine how AI works and how it can be controlled.

Recommended topics include, but are not limited to, the following:

  • Technical applications of AI in maritime applications;
  • Defense measures used by the navy against AI systems;
  • Autonomous and robotic systems in the maritime sector;
  • HMI interfaces in the maritime industry, and training for them;
  • Remote sensing and video processing in maritime traffic surveillance;
  • AI in maritime logistics and finances.

Prof. Dr. Igor Vujović
Dr. Joško Šoda
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. Applied Sciences 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 2400 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

  • maritime video surveillance
  • harbor traffic
  • remote sensing
  • artificial intelligence applications
  • AI maritime logistics
  • contermeasures
  • AI application consequences
  • human–machine interface

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Published Papers (3 papers)

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Research

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20 pages, 1775 KB  
Article
AI-Driven Energy Management for Sustainable Transformation of Recreational Boats: A Simulation Study for the Croatian Adriatic Coast
by Jasmin Ćelić, Aleksandar Cuculić, Ivan Panić and Marko Vukšić
Appl. Sci. 2026, 16(9), 4186; https://doi.org/10.3390/app16094186 - 24 Apr 2026
Viewed by 160
Abstract
Croatia hosts one of the most intensive recreational boating activities in the Mediterranean, with over 134,600 registered vessels along 5835 km of Adriatic coastline. This paper presents an AI-driven simulation framework for evaluating electrification pathways for the Croatian recreational vessel fleet. A key [...] Read more.
Croatia hosts one of the most intensive recreational boating activities in the Mediterranean, with over 134,600 registered vessels along 5835 km of Adriatic coastline. This paper presents an AI-driven simulation framework for evaluating electrification pathways for the Croatian recreational vessel fleet. A key contribution is the explicit treatment of the AIS data gap: recreational vessels in Croatia are not required to carry AIS transponders, so synthetic operational profiles calibrated from manufacturer specifications and verified economic data are used instead. Six machine learning architectures are compared for vessel energy demand forecasting, with a proposed Transformer-based model achieving the best simulated performance. Fleet-weighted Monte Carlo simulation across three electrification scenarios suggests that an AI-optimised hybrid configuration can, subject to use intensity, reduce per-vessel CO2 emissions by up to 56.8% relative to conventional engines. Techno-economic analysis shows payback periods ranging from over 15 years for low-use private owners to 7–9 years for charter operators, supporting targeted incentive design. The framework is intended to be transferable to other Mediterranean coastal regions facing comparable data and operational constraints. Full article
(This article belongs to the Special Issue AI Applications in the Maritime Sector)
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19 pages, 1004 KB  
Article
Early Anomaly Detection in Maritime Refrigerated Containers Using a Hybrid Digital Twin and Deep Learning Framework
by Marko Vukšić, Jasmin Ćelić, Dario Ogrizović and Ana Perić Hadžić
Appl. Sci. 2026, 16(4), 1887; https://doi.org/10.3390/app16041887 - 13 Feb 2026
Cited by 1 | Viewed by 516
Abstract
Maritime refrigerated containers operate under harsh and highly variable conditions, where gradual equipment degradation can lead to temperature excursions, cargo losses, and operational disruptions. In current practice, monitoring relies largely on threshold-based temperature alarms, which are reactive and provide limited insight into early [...] Read more.
Maritime refrigerated containers operate under harsh and highly variable conditions, where gradual equipment degradation can lead to temperature excursions, cargo losses, and operational disruptions. In current practice, monitoring relies largely on threshold-based temperature alarms, which are reactive and provide limited insight into early abnormal behaviour. This study proposes a hybrid framework for early anomaly detection in maritime refrigerated containers that combines a lightweight physics-based digital twin with a deep learning anomaly detector trained exclusively on fault-free operation. The approach is designed for shipboard constraints and uses only controller-level signals augmented by locally derived features, enabling low-complexity edge execution. The digital twin produces physically interpretable temperature residuals, while a convolutional autoencoder learns normal multivariate operating patterns and flags deviations via reconstruction error. Both indicators are integrated using conservative persistence gating to suppress short-lived transients typical of maritime operation. The framework is evaluated in a simulation environment calibrated to representative reefer thermal dynamics under variable ambient conditions and progressive fault injection across gradual and abrupt fault categories. Results indicate earlier and operationally credible detection compared to conventional alarms, supporting practical predictive maintenance in maritime cold-chain logistics. Full article
(This article belongs to the Special Issue AI Applications in the Maritime Sector)
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Other

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20 pages, 2270 KB  
Systematic Review
Infrared Thermography in Maritime Systems: A Systematic Review
by Lucija Tadić, Ivana Golub Medvešek, Igor Vujović and Joško Šoda
Appl. Sci. 2025, 15(23), 12551; https://doi.org/10.3390/app152312551 - 26 Nov 2025
Cited by 1 | Viewed by 1145
Abstract
The maritime industry is undergoing a digital transformation, in which predictive maintenance and intelligent diagnostics play a crucial role in enhancing operational safety and efficiency. This paper investigates the application of infrared thermography (IRT) for fault detection and condition monitoring of ship machinery, [...] Read more.
The maritime industry is undergoing a digital transformation, in which predictive maintenance and intelligent diagnostics play a crucial role in enhancing operational safety and efficiency. This paper investigates the application of infrared thermography (IRT) for fault detection and condition monitoring of ship machinery, with particular emphasis on its integration within condition-based and predictive maintenance frameworks. A systematic review was conducted in accordance with the PRISMA 2020 methodology, analyzing 210 publications retrieved from the Web of Science (WoS), Scopus, and Google Scholar databases to identify prevailing technological trends and research gaps. The results indicate that IRT enables early detection of critical faults such as overheating, insulation degradation, and poor electrical connections, thereby reducing unplanned downtime and improving system reliability. When integrated with artificial intelligence (AI), deep learning (DL), and convolutional neural networks (CNNs), diagnostic accuracy can be automated through enhanced data interpretation. Despite its proven effectiveness, standardized protocols and real-world validation of IRT–AI systems remain limited in the maritime sector. IRT is therefore recognized as a key enabler of safer, smarter, and more sustainable ship maintenance within the broader maritime digitalization framework. Full article
(This article belongs to the Special Issue AI Applications in the Maritime Sector)
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