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Application of Advanced Technologies in Maritime Safety—Second Edition
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Application of Advanced Technologies in Maritime Safety—Second Edition

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 September 2025 | Viewed by 3142

Special Issue Editors

Prof. Dr. Degang Xu

E-Mail Website
Guest Editor
School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
Interests: laser technology; spectral imaging technology; marine Lidar technology
Special Issues, Collections and Topics in MDPI journals
Dr. Chenxu Wang

E-Mail Website
Guest Editor
School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
Interests: ocean engineering; fluid-structure interaction; ocean data analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jiaxuan Yang

E-Mail Website
Guest Editor
Navigation College, Dalian Maritime University, Dalian 116026, China
Interests: maritime safety assurance; intelligent navigation technology; autonomous waterway transportation system; ship traffic organization and management; intelligent ship navigation; maritime big data analysis and integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of technologies such as mapping technology, positioning technology, imaging technology, communication technology, and intelligent equipment, or the hull’s own performance, have all profoundly promoted improvements in maritime safety. At the same time, the strong demand for advanced marine technologies as a result of the development of maritime engineering has sparked the creation of new technologies. The surge of autonomous navigation has also put forward higher requirements for technology and provided a stage for the display and application of these new technologies.

Now, Journal of Marine Science and Engineering is pleased to announce a new Special Issue, entitled “Application of Advanced Technologies in Maritime Safety—Second Edition”. This is based on the great success of our previous Special Issue with the same title: “Application of Advanced Technologies in Maritime Safety”.

This Special Issue focuses on recent developments in advanced technologies which can be applied to enhance maritime safety. It also provides a platform for both areas to communicate and converge. Topics of interest include, but are not limited to, the following:

  • Sensing and communicating in maritime;
  • High performance of ship hull;
  • Automatic identification system;
  • Marine measuring;
  • Sonar system integrating sound with laser;
  • Ship collision avoidance.

Prof. Dr. Degang Xu
Dr. Chenxu Wang
Prof. Dr. Jiaxuan Yang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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.

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

Published Papers (4 papers)

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Research

25 pages, 20571 KiB  
Article
Mid-Water Ocean Current Field Estimation Using Radial Basis Functions Based on Multibeam Bathymetric Survey Data for AUV Navigation
by Jiawen Liu, Kaixuan Wang, Shuai Chang and Lin Pan
J. Mar. Sci. Eng. 2025, 13(5), 841; https://doi.org/10.3390/jmse13050841 - 24 Apr 2025
Viewed by 164
Abstract
Autonomous Underwater Vehicle (AUV) navigation relies on bottom-tracking velocity from Doppler Velocity Log (DVL) for positioning through dead-reckoning or aiding Strapdown Inertial Navigation System (SINS). In mid-water environments, the distance between the AUV and the seafloor exceeds the detection range of DVL, causing [...] Read more.
Autonomous Underwater Vehicle (AUV) navigation relies on bottom-tracking velocity from Doppler Velocity Log (DVL) for positioning through dead-reckoning or aiding Strapdown Inertial Navigation System (SINS). In mid-water environments, the distance between the AUV and the seafloor exceeds the detection range of DVL, causing failure of bottom-tracking and leaving only water-relative velocity available. This makes unknown ocean currents a significant error source that leads to substantial cumulative positioning errors. This paper proposes a method for mid-water ocean current estimation using multibeam bathymetric survey data. First, the method models the regional unknown current field using radius basis functions (RBFs) and establishes an AUV dead-reckoning model incorporating the current field. The RBF model inherently satisfies ocean current incompressibility. Subsequently, by dividing the multibeam bathymetric point cloud data surveyed by the AUV into submaps and performing a terrain-matching algorithm, relative position observations among different AUV positions can be constructed. These observations are then utilized to estimate the RBF parameters of the current field within the navigation model. Numerical simulations and experiments based on real-world bathymetric and ocean current data demonstrate that the proposed method can effectively capture the complex spatial variations in ocean currents, contributing to the accurate reconstruction of the mid-water current field and significant improvement in positioning accuracy. Full article
(This article belongs to the Special Issue Application of Advanced Technologies in Maritime Safety—Second Edition)
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25 pages, 13401 KiB  
Article
Enhanced U-Net for Underwater Laser Range-Gated Image Restoration: Boosting Underwater Target Recognition
by Peng Liu, Shuaibao Chen, Wei He, Jue Wang, Liangpei Chen, Yuguang Tan, Dong Luo, Wei Chen and Guohua Jiao
J. Mar. Sci. Eng. 2025, 13(4), 803; https://doi.org/10.3390/jmse13040803 - 17 Apr 2025
Viewed by 183
Abstract
Underwater optical imaging plays a crucial role in maritime safety, enabling reliable navigation, efficient search and rescue operations, precise target recognition, and robust military reconnaissance. However, conventional underwater imaging methods often suffer from severe backscattering noise, limited detection range, and reduced image clarity—challenges [...] Read more.
Underwater optical imaging plays a crucial role in maritime safety, enabling reliable navigation, efficient search and rescue operations, precise target recognition, and robust military reconnaissance. However, conventional underwater imaging methods often suffer from severe backscattering noise, limited detection range, and reduced image clarity—challenges that are exacerbated in turbid waters. To address these issues, Underwater Laser Range-Gated Imaging has emerged as a promising solution. By selectively capturing photons within a controlled temporal gate, this technique effectively suppresses backscattering noise-enhancing image clarity, contrast, and detection range. Nevertheless, residual noise within the imaging slice can still degrade image quality, particularly in challenging underwater conditions. In this study, we propose an enhanced U-Net neural network designed to mitigate noise interference in underwater laser range-gated images, improving target recognition performance. Built upon the U-Net architecture with added residual connections, our network combines a VGG16-based perceptual loss with Mean Squared Error (MSE) as the loss function, effectively capturing high-level semantic features while preserving critical target details during reconstruction. Trained on a semi-synthetic grayscale dataset containing synthetically degraded images paired with their reference counterparts, the proposed approach demonstrates improved performance compared to several existing underwater image restoration methods in our experimental evaluations. Through comprehensive qualitative and quantitative evaluations, underwater target detection experiments, and real-world oceanic validations, our method demonstrates significant potential for advancing maritime safety and related applications. Full article
(This article belongs to the Special Issue Application of Advanced Technologies in Maritime Safety—Second Edition)
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26 pages, 12481 KiB  
Article
EGM-YOLOv8: A Lightweight Ship Detection Model with Efficient Feature Fusion and Attention Mechanisms
by Ying Li and Siwen Wang
J. Mar. Sci. Eng. 2025, 13(4), 757; https://doi.org/10.3390/jmse13040757 - 10 Apr 2025
Viewed by 254
Abstract
Accurate and real-time ship detection is crucial for intelligent waterborne transportation systems. However, detecting ships across various scales remains challenging due to category diversity, shape similarity, and complex environmental interference. In this work, we propose EGM-YOLOv8, a lightweight and enhanced model for real-time [...] Read more.
Accurate and real-time ship detection is crucial for intelligent waterborne transportation systems. However, detecting ships across various scales remains challenging due to category diversity, shape similarity, and complex environmental interference. In this work, we propose EGM-YOLOv8, a lightweight and enhanced model for real-time ship detection. We integrate the Efficient Channel Attention (ECA) module to improve feature extraction and employ a lightweight Generalized Efficient Layer Aggregation Network (GELAN) combined with Path Aggregation Network (PANet) for efficient multi-scale feature fusion. Additionally, we introduce MPDIoU, a minimum-distance-based loss function, to enhance localization accuracy. Compared to YOLOv8, EGM-YOLOv8 reduces the number of parameters by 13.57%, reduces the computational complexity by 11.05%, and improves the recall rate by 1.13%, demonstrating its effectiveness in maritime environments. The model is well-suited for deployment on resource-constrained devices, balancing precision and efficiency for real-time applications. Full article
(This article belongs to the Special Issue Application of Advanced Technologies in Maritime Safety—Second Edition)
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23 pages, 16111 KiB  
Article
Advanced Human Reliability Analysis Approach for Ship Convoy Operations via a Model of IDAC and DBN: A Case from Ice-Covered Waters
by Yongtao Xi, Xiang Zhang, Bing Han, Yu Zhu, Cunlong Fan and Eunwoo Kim
J. Mar. Sci. Eng. 2024, 12(9), 1536; https://doi.org/10.3390/jmse12091536 - 3 Sep 2024
Cited by 2 | Viewed by 1392
Abstract
The melting of Arctic ice has facilitated the successful navigation of merchant ships through the Arctic route, often requiring icebreakers for assistance. To reduce the risk of accidents between merchant vessels and icebreakers stemming from human errors during operations, this paper introduces an [...] Read more.
The melting of Arctic ice has facilitated the successful navigation of merchant ships through the Arctic route, often requiring icebreakers for assistance. To reduce the risk of accidents between merchant vessels and icebreakers stemming from human errors during operations, this paper introduces an enhanced human reliability assessment approach. This method utilizes the Dynamic Bayesian Network (DBN) model, integrated with the information, decision, and action in crew context (IDAC) framework. First, a qualitative analysis of crew maneuvering behavior in scenarios involving a collision with the preceding vessel during icebreaker assistance is conducted using the IDAC model. Second, the D–S evidence theory and cloud models are integrated to process multi-source subjective data. Finally, the human error probability of crew members is quantified using the DBN. The research results indicate that during convoy operations, the maximum probability that the officer on watch (OOW) chooses an incorrect deceleration strategy is 8.259×10−2 and the collision probability is 4.129×10−3. Furthermore, this study also found that the factors of Team Effectiveness and Knowledge/Abilities during convoy operations have the greatest impact on collision occurrence. This research provides important guidance and recommendations for the safe navigation of merchant ships in the Arctic waters. By reducing human errors and adopting appropriate preventive measures, the risk of collisions between merchant ships and icebreakers can be significantly decreased. Full article
(This article belongs to the Special Issue Application of Advanced Technologies in Maritime Safety—Second Edition)
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