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Application of Artificial Intelligence in Maritime Transportation
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Application of Artificial Intelligence in Maritime Transportation

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: closed (15 December 2023) | Viewed by 31541

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Xinqiang Chen

E-Mail Website
Guest Editor
Insitute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: video data-driven intelligent transportation environment perception and understanding; large-scale transportation data analysis (traffic flow data, AIS, etc.); smart ship/autonomous port
Special Issues, Collections and Topics in MDPI journals
Dr. Dongfang Ma

E-Mail Website
Guest Editor
Institute of Intelligent Transportation System, Zhejiang University, Hangzhou 310058, China
Interests: maritime data mining; intelligent control theory and method; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Ryan Wen Liu

E-Mail Website
Guest Editor
School of Navigation, Wuhan University of Technology, Wuhan 430063, China
Interests: computer vision (unmanned vehicle); ship trajectory data mining; maritime intelligent transportation system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of cargo carriages is mainly implemented by ships worldwide as a manner of cost-effective transport. The recruitment of new ship crews has become difficult due to piracy, staying away from home for long periods of time, etc. The new development of artificial intelligence (AI) techniques demonstrates tremendous potential in alleviating the above-mentioned disadvantages. It can be envisioned that AI techniques may require fewer on-board ship crew members, whilst the maritime traffic safety and efficiency will be obviously enhanced as well. More specifically, AI techniques will be introduced to revolutionize the shipping industry by conducting the following tasks: maritime traffic situation awareness, automated ship controlling, optimal ship trajectory planning, ship-shore-vehicle collaboration, vehicle scheduling for automated container terminal (ACT), intelligent maritime supervision and management, multi-ship cooperative, etc.

The Special Issue aims to invite studies to effectively guide the future planning, design, construction, and application of maritime transportation system, and provides strong support for cultivating new-era maritime transportation industry with support of varied data sources (video, automatic identification system (AIS), radar, etc.). We invite full paper submissions fitting the general theme of “application of artificial intelligence in maritime transportation”. Moreover, we encourage submissions from a broad range of research fields related to maritime transportation issues. 

  • Intelligent maritime traffic situation awareness. 
  • Ship behavior identification and prediction via varied maritime traffic data sources.
  • Optimal traffic controlling via cooperation of vehicle, ship and management center.
  • Ship visual navigation and mooring via multiple maritime data sources.
  • Maritime traffic safety analysis.
  • Ship and vehicle fleet controlling and trajectory planning.
  • Vehicle scheduling and optimization related issues in the ACT era.
  • Carbon-emission-motivated trajectory planning, scheduling, and controlling, etc.
  • Traffic flow prediction and analysis. 

Dr. Xinqiang Chen
Dr. Dongfang Ma
Dr. Ryan Wen Liu
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.

Keywords

  • maritime transportation system
  • intelligent situational awareness
  • ship–vehicle–port cooperation
  • smart ship
  • automated container terminal

Published Papers (22 papers)

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Editorial

Jump to: Research

4 pages, 155 KiB  
Editorial
Application of Artificial Intelligence in Maritime Transportation
by Xinqiang Chen, Dongfang Ma and Ryan Wen Liu
J. Mar. Sci. Eng. 2024, 12(3), 439; https://doi.org/10.3390/jmse12030439 - 01 Mar 2024
Viewed by 1053
Abstract
Maritime logistics and supply chain management have become more complicated due to economic globalization development [...] Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)

Research

Jump to: Editorial

17 pages, 2800 KiB  
Article
Automatic Identification System-Based Prediction of Tanker and Cargo Estimated Time of Arrival in Narrow Waterways
by Homayoon Arbabkhah, Atefe Sedaghat, Masood Jafari Kang and Maryam Hamidi
J. Mar. Sci. Eng. 2024, 12(2), 215; https://doi.org/10.3390/jmse12020215 - 25 Jan 2024
Viewed by 853
Abstract
In maritime logistics, accurately predicting the Estimated Time of Arrival (ETA) of vessels is pivotal for optimizing port operations and the global supply chain. This study proposes a machine learning method for predicting ETA, drawing on historical Automatic Identification System (AIS) data spanning [...] Read more.
In maritime logistics, accurately predicting the Estimated Time of Arrival (ETA) of vessels is pivotal for optimizing port operations and the global supply chain. This study proposes a machine learning method for predicting ETA, drawing on historical Automatic Identification System (AIS) data spanning 2018 to 2020. The proposed framework includes a preprocessing module for extracting, transforming, and applying feature engineering to raw AIS data, alongside a modeling module that employs an XGBoost model to accurately estimate vessel travel times. The framework’s efficacy was validated using AIS data from the Port of Houston, and the results indicate that the model can estimate travel times with a Mean Absolute Percentage Error (MAPE) of just 5%. Moreover, the model retains consistent accuracy in a simplified form, pointing towards the potential for reduced complexity and increased generalizability in maritime ETA predictions. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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22 pages, 9457 KiB  
Article
Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data
by Atefe Sedaghat, Homayoon Arbabkhah, Masood Jafari Kang and Maryam Hamidi
J. Mar. Sci. Eng. 2024, 12(1), 152; https://doi.org/10.3390/jmse12010152 - 12 Jan 2024
Cited by 3 | Viewed by 818
Abstract
This research introduces an online system for monitoring maritime traffic, aimed at tracking vessels in water routes and predicting their subsequent locations in real time. The proposed framework utilizes an Extract, Transform, and Load (ETL) pipeline to dynamically process AIS data by cleaning, [...] Read more.
This research introduces an online system for monitoring maritime traffic, aimed at tracking vessels in water routes and predicting their subsequent locations in real time. The proposed framework utilizes an Extract, Transform, and Load (ETL) pipeline to dynamically process AIS data by cleaning, compressing, and enhancing it with additional attributes such as online traffic volume, origin/destination, vessel trips, trip direction, and vessel routing. This processed data, enriched with valuable details, serves as an alternative to raw AIS data stored in a centralized database. For user interactions, a user interface is designed to query the database and provide real-time information on a map-based interface. To deal with false or missing AIS records, two methods, dead reckoning and machine learning techniques, are employed to anticipate the trajectory of the vessel in the next time steps. To evaluate each method, several metrics are used, including R squared, mean absolute error, mean offset, and mean offset from the centerline. The functionality of the proposed system is showcased through a case study conducted in the Gulf Intracoastal Waterway (GIWW). Three years of AIS data are collected and processed as a simulated API to transmit AIS records every five minutes. According to our results, the Seq2Seq model exhibits strong performance (0.99 R squared and an average offset of ~1400 ft). However, the second scenario, dead reckoning, proves comparable to the Seq2Seq model as it involves recalculating vessel headings by comparing each data point with the previous one. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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19 pages, 13261 KiB  
Article
Maritime Transport Network in Korea: Spatial-Temporal Density and Path Planning
by Jeong-Seok Lee, Tae-Hoon Kim and Yong-Gil Park
J. Mar. Sci. Eng. 2023, 11(12), 2364; https://doi.org/10.3390/jmse11122364 - 14 Dec 2023
Viewed by 768
Abstract
The increase in maritime traffic and vessel size has strengthened the need for economical and safe maritime transportation networks. Currently, ship path planning is based on past experience and shortest route usage. However, the increasing complexity of the marine environment and the development [...] Read more.
The increase in maritime traffic and vessel size has strengthened the need for economical and safe maritime transportation networks. Currently, ship path planning is based on past experience and shortest route usage. However, the increasing complexity of the marine environment and the development of autonomous ships require automatic shortest path generation based on maritime traffic networks. This paper proposes an efficient shortest path planning method using Dijkstra’s algorithm based on a maritime traffic network dataset created by extracting maritime traffic routes through a spatial-temporal density analysis of large-scale AIS data and Delaunay triangulation. Additionally, the depth information of all digital charts in Korea was set as a safety contour to support safe path planning. The proposed network-based shortest path planning method was compared with the path planning and sailing distance of a training ship, and compliance with maritime laws was verified. The results demonstrate the practicality and safety of the proposed method, which can enable the establishment of a safe and efficient maritime transportation network along with the development of autonomous ships. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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16 pages, 23258 KiB  
Article
Ship Detection under Low-Visibility Weather Interference via an Ensemble Generative Adversarial Network
by Xinqiang Chen, Chenxin Wei, Zhengang Xin, Jiansen Zhao and Jiangfeng Xian
J. Mar. Sci. Eng. 2023, 11(11), 2065; https://doi.org/10.3390/jmse11112065 - 29 Oct 2023
Cited by 1 | Viewed by 915
Abstract
Maritime ship detection plays a crucial role in smart ships and intelligent transportation systems. However, adverse maritime weather conditions, such as rain streak and fog, can significantly impair the performance of visual systems for maritime traffic. These factors constrain the performance of traffic [...] Read more.
Maritime ship detection plays a crucial role in smart ships and intelligent transportation systems. However, adverse maritime weather conditions, such as rain streak and fog, can significantly impair the performance of visual systems for maritime traffic. These factors constrain the performance of traffic monitoring systems and ship-detection algorithms for autonomous ship navigation, affecting maritime safety. The paper proposes an approach to resolve the problem by visually removing rain streaks and fog from images, achieving an integrated framework for accurate ship detection. Firstly, the paper employs an attention generation network within an adversarial neural network to focus on the distorted regions of the degraded images. The paper also utilizes a contextual encoder to infer contextual information within the distorted regions, enhancing the credibility of image restoration. Secondly, a weighted bidirectional feature pyramid network (BiFPN) is introduced to achieve rapid multi-scale feature fusion, enhancing the accuracy of maritime ship detection. The proposed GYB framework was validated using the SeaShip dataset. The experimental results show that the proposed framework achieves an average accuracy of 96.3%, a recall of 95.35%, and a harmonic mean of 95.85% in detecting maritime traffic ships under rain-streak and foggy-weather conditions. Moreover, the framework outperforms state-of-the-art ship detection methods in such challenging weather scenarios. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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18 pages, 7713 KiB  
Article
Research on Visual Perception for Coordinated Air–Sea through a Cooperative USV-UAV System
by Chen Cheng, Dong Liu, Jin-Hui Du and Yong-Zheng Li
J. Mar. Sci. Eng. 2023, 11(10), 1978; https://doi.org/10.3390/jmse11101978 - 12 Oct 2023
Cited by 2 | Viewed by 909
Abstract
The identification and classification of obstacles in navigable and non-navigable regions, as well as the measurement of distances, are crucial topics of investigation in the field of autonomous navigation for unmanned surface vehicles (USVs). Currently, USVs mostly rely on LiDAR and ultrasound technology [...] Read more.
The identification and classification of obstacles in navigable and non-navigable regions, as well as the measurement of distances, are crucial topics of investigation in the field of autonomous navigation for unmanned surface vehicles (USVs). Currently, USVs mostly rely on LiDAR and ultrasound technology for the purpose of detecting impediments that exist on water surfaces. However, it is worth noting that these approaches lack the capability to accurately discern the precise nature or classification of those obstacles. Nevertheless, the limited optical range of unmanned vessels hinders their ability to comprehensively perceive the entirety of the surrounding information. A cooperative USV-UAV system is proposed to ensure the visual perception ability of USVs. The multi-object recognition, semantic segmentation, and obstacle ranging through USV and unmanned aerial vehicle (UAV) perspectives are selected to validate the performance of a cooperative USV-UAV system. The you only look once-X (YOLOX) model, the proportional–integral–derivative-NET (PIDNet) model, and distance measurements based on a monocular camera are utilized to realize these problems. The results indicate that by integrating the viewpoints of USVs and UAVs, a collaborative USV-UAV system, employing the aforementioned methods, can successfully detect and classify different objects surrounding the USV. Additionally, it can differentiate between navigable and non-navigable regions for unmanned vessels through visual recognition, while accurately determining the distance between the USV and obstacles. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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19 pages, 4272 KiB  
Article
Multi-Feature Fusion-Guided Low-Visibility Image Enhancement for Maritime Surveillance
by Wenbo Zhou, Bin Li and Guoling Luo
J. Mar. Sci. Eng. 2023, 11(8), 1625; https://doi.org/10.3390/jmse11081625 - 20 Aug 2023
Cited by 1 | Viewed by 894
Abstract
Low-visibility maritime image enhancement is essential for maritime surveillance in extreme weathers. However, traditional methods merely optimize contrast while ignoring image features and color recovery, which leads to subpar enhancement outcomes. The majority of learning-based methods attempt to improve low-visibility images by only [...] Read more.
Low-visibility maritime image enhancement is essential for maritime surveillance in extreme weathers. However, traditional methods merely optimize contrast while ignoring image features and color recovery, which leads to subpar enhancement outcomes. The majority of learning-based methods attempt to improve low-visibility images by only using local features extracted from convolutional layers, which significantly improves performance but still falls short of fully resolving these issues. Furthermore, the computational complexity is always sacrificed for larger receptive fields and better enhancement in CNN-based methods. In this paper, we propose a multiple-feature fusion-guided low-visibility enhancement network (MFF-Net) for real-time maritime surveillance, which extracts global and local features simultaneously to guide the reconstruction of the low-visibility image. The quantitative and visual experiments on both standard and maritime-related datasets demonstrate that our MFF-Net provides superior enhancement with noise reduction and color restoration, and has a fast computational speed. Furthermore, the object detection experiment indicates practical benefits for maritime surveillance. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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21 pages, 4476 KiB  
Article
Application of an Encoder–Decoder Model with Attention Mechanism for Trajectory Prediction Based on AIS Data: Case Studies from the Yangtze River of China and the Eastern Coast of the U.S
by Licheng Zhao, Yi Zuo, Tieshan Li and C. L. Philip Chen
J. Mar. Sci. Eng. 2023, 11(8), 1530; https://doi.org/10.3390/jmse11081530 - 31 Jul 2023
Cited by 3 | Viewed by 1225
Abstract
With the rapid growth of shipping volumes, ship navigation and path planning have attracted increased attention. To design navigation routes and avoid ship collisions, accurate ship trajectory prediction based on automatic identification system data is required. Therefore, this study developed an encoder–decoder learning [...] Read more.
With the rapid growth of shipping volumes, ship navigation and path planning have attracted increased attention. To design navigation routes and avoid ship collisions, accurate ship trajectory prediction based on automatic identification system data is required. Therefore, this study developed an encoder–decoder learning model for ship trajectory prediction, to avoid ship collisions. The proposed model includes long short-term memory units and an attention mechanism. Long short-term memory can extract relationships between the historical trajectory of a ship and the current state of encountered ships. Simultaneously, the global attention mechanism in the proposed model can identify interactions between the output and input trajectory sequences, and a multi-head self-attention mechanism in the proposed model is used to learn the feature fusion representation between the input trajectory sequences. Six case studies of trajectory prediction for ship collision avoidance from the Yangtze River of China and the eastern coast of the U.S. were investigated and compared. The results showed that the average mean absolute errors of our model were much lower than those of the classical neural networks and other state-of-the-art models that included attention mechanisms. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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22 pages, 8015 KiB  
Article
Identification of Spoofing Ships from Automatic Identification System Data via Trajectory Segmentation and Isolation Forest
by Hailin Zheng, Qinyou Hu, Chun Yang, Qiang Mei, Peng Wang and Kelong Li
J. Mar. Sci. Eng. 2023, 11(8), 1516; https://doi.org/10.3390/jmse11081516 - 29 Jul 2023
Viewed by 848
Abstract
Outliers of ship trajectory from the Automatic Identification System (AIS) onboard a ship will affect the accuracy of maritime situation awareness, especially for a regular ship trajectory mixed with a spoofing ship, which has an unauthorized Maritime Mobile Service Identification code (MMSI) owned [...] Read more.
Outliers of ship trajectory from the Automatic Identification System (AIS) onboard a ship will affect the accuracy of maritime situation awareness, especially for a regular ship trajectory mixed with a spoofing ship, which has an unauthorized Maritime Mobile Service Identification code (MMSI) owned by a regular ship. As has been referred to in the literature, the trajectory of these spoofing ships would simply be removed, and more AIS data would be lost. The pre-processing of AIS data should aim to retain more information, which is more helpful in maritime situation awareness for the Maritime Safety Administration (MSA). Through trajectory feature mining, it has been found that there are obvious differences between the trajectory of a regular ship and that of a regular ship mixed with a spoofing ship, such as in terms of speed and distance between adjacent trajectory points. However, there can be a long update time interval in the results of severe missing trajectories of a ship, bringing challenges in terms of the identification of spoofing ships. In order to accurately divide the regular ship trajectory and spoofing ship trajectory, combined with trajectory segmentation by the update time interval threshold, the isolation forest was adopted in this work to train the labeled trajectory point of a regular ship mixed with a spoofing ship. The experimental results show that the average accuracy of the identification of spoofing ships using isolation forest is 88.4%, 91%, 93.1%, and 93.3%, corresponding to different trajectory segmentation by update time intervals (5 h, 10 h, 15 h, and 20 h). The research conducted in this study can almost eliminate the outliers of ship trajectory, and it also provides help for maritime situation awareness for the MSA. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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19 pages, 3781 KiB  
Article
An Improved A-Star Ship Path-Planning Algorithm Considering Current, Water Depth, and Traffic Separation Rules
by Rong Zhen, Qiyong Gu, Ziqiang Shi and Yongfeng Suo
J. Mar. Sci. Eng. 2023, 11(7), 1439; https://doi.org/10.3390/jmse11071439 - 18 Jul 2023
Cited by 7 | Viewed by 1855
Abstract
The influence of the maritime environment such as water currents, water depth, and traffic separation rules should be considered when conducting ship path planning. Additionally, the maneuverability constraints of the ship play a crucial role in navigation. Addressing the limitations of the traditional [...] Read more.
The influence of the maritime environment such as water currents, water depth, and traffic separation rules should be considered when conducting ship path planning. Additionally, the maneuverability constraints of the ship play a crucial role in navigation. Addressing the limitations of the traditional A-star algorithm in ship path planning, this paper proposes an improved A-star algorithm. Specifically, this paper examines the factors influencing ship navigation safety, and develops a risk model that takes into account water currents, water depth, and obstacles. The goal is to mitigate the total risk of ship collisions and grounding. Secondly, a traffic model is designed to ensure that the planned path adheres to the traffic separation rules and reduces the risk of collision with incoming ships. Then, a turning model and smoothing method are designed to make the generated path easy to track and control for the ship. To validate the effectiveness of the proposed A-star ship path-planning algorithm, three cases are studied in simulations and representative operational scenarios. The results of the cases demonstrate that the proposed A-star ship path-planning algorithm can better control the distance to obstacles, effectively avoid shallow water areas, and comply with traffic separation rules. The safety level of the path is effectively improved. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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19 pages, 6216 KiB  
Article
Ship Autonomous Berthing Simulation Based on Covariance Matrix Adaptation Evolution Strategy
by Guoquan Chen, Jian Yin and Shenhua Yang
J. Mar. Sci. Eng. 2023, 11(7), 1400; https://doi.org/10.3390/jmse11071400 - 11 Jul 2023
Cited by 2 | Viewed by 1114
Abstract
Existing research on auto-berthing of ships has mainly focused on the design and implementation of controllers for automatic berthing. For the real automatic docking processes, not only do external environmental perturbations need to be taken into account but also motion paths, docking strategies [...] Read more.
Existing research on auto-berthing of ships has mainly focused on the design and implementation of controllers for automatic berthing. For the real automatic docking processes, not only do external environmental perturbations need to be taken into account but also motion paths, docking strategies and ship mechanical constraints, which are important influential factors to measure autonomous docking methods. Through a literature review of ship path planning and motion control for automatic berthing, it is found that many studies ignore the interference of the actual navigational environment, especially for ships sailing at slow speed when berthing, or do not consider the physical constraints of the steering gear and the main engine. In this paper, we propose a hybrid approach for autonomous berthing control systems based on a Linear Quadratic Regulator (LQR) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which systematically addresses the problems involved in the berthing process, such as path planning, optimal control, adaptive berthing strategies, dynamic environmental perturbations and physically enforced structural constraints. The berthing control system based on the LQR and modified LQR-CMA-ES have been validated by simulation work. The simulation results show that the proposed method is able to achieve the automatic docking of the ship well and the system is robust and well adapted to environmental disturbances at slow speed when docking. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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18 pages, 1507 KiB  
Article
Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks
by Yong Li, Zhaoxuan Li, Qiang Mei, Peng Wang, Wenlong Hu, Zhishan Wang, Wenxin Xie, Yang Yang and Yuhaoran Chen
J. Mar. Sci. Eng. 2023, 11(7), 1379; https://doi.org/10.3390/jmse11071379 - 06 Jul 2023
Cited by 3 | Viewed by 1539
Abstract
The intelligent maritime transportation system has emerged as a pivotal component in port management, owing to the rapid advancements in artificial intelligence and big data technology. Its essence lies in the application of digital modeling techniques, which leverage extensive ship data to facilitate [...] Read more.
The intelligent maritime transportation system has emerged as a pivotal component in port management, owing to the rapid advancements in artificial intelligence and big data technology. Its essence lies in the application of digital modeling techniques, which leverage extensive ship data to facilitate efficient operations. In this regard, effective modeling and accurate prediction of the fluctuation patterns of ship traffic in multiple port regions will provide data support for trade analysis, port construction planning, and traffic safety management. In order to better express the potential interdependencies between ports, inspired by graph neural networks, this paper proposes a data-driven approach to construct a multi-port network and designs a spatiotemporal graph neural network model. The model incorporates graph attention networks and a dilated causal convolutional architecture to capture the temporal and spatial dimensions of traffic variation patterns. It also employs a gated-mechanism-based spatiotemporal bi-dimensional feature fusion strategy to handle the potential unequal relationships between the two dimensions of features. Compared to existing methods for port traffic prediction, this model fully considers the network characteristics of the overall port and fills the research gap in multi-port scenarios. In the experiments, real port ship traffic datasets were constructed using data from the Automatic Identification System (AIS) and port geographical information data for model validation. The results demonstrate that the model exhibits outstanding robustness and performs well in predicting traffic in multiple sub-regional port clusters. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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19 pages, 14605 KiB  
Article
A Ship Tracking and Speed Extraction Framework in Hazy Weather Based on Deep Learning
by Zhenzhen Zhou, Jiansen Zhao, Xinqiang Chen and Yanjun Chen
J. Mar. Sci. Eng. 2023, 11(7), 1353; https://doi.org/10.3390/jmse11071353 - 02 Jul 2023
Cited by 3 | Viewed by 1429
Abstract
Obtaining ship navigation information from maritime videos can significantly improve maritime supervision efficiency and enable timely safety warnings. Ship detection and tracking are essential technologies for mining video information. However, current research focused on these advanced vision tasks in maritime supervision is not [...] Read more.
Obtaining ship navigation information from maritime videos can significantly improve maritime supervision efficiency and enable timely safety warnings. Ship detection and tracking are essential technologies for mining video information. However, current research focused on these advanced vision tasks in maritime supervision is not sufficiently comprehensive. Taking into account the application of ship detection and tracking technology, this study proposes a deep learning-based ship speed extraction framework under the haze environment. First, a lightweight convolutional neural network (CNN) is used to remove haze from images. Second, the YOLOv5 algorithm is used to detect ships in dehazed marine images, and a simple online and real-time tracking method with a Deep association metric (Deep SORT) is used to track ships. Then, the ship’s displacement in the images is calculated based on the ship’s trajectory. Finally, the speed of the ships is estimated by calculating the mapping relationship between the image space and real space. Experiments demonstrate that the method proposed in this study effectively reduces haze interference in maritime videos, thereby enhancing the image quality while extracting the ship’s speed. The mean squared error (MSE) for multiple scenes is 0.3 Kn on average. The stable extraction of ship speed from the video achieved in this study holds significant value in further ensuring the safety of ship navigation. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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27 pages, 3475 KiB  
Article
A System-Theory and Complex Network-Fused Approach to Analyze Vessel–Wind Turbine Allisions in Offshore Wind Farm Waters
by Kai Yan, Yanhui Wang, Wenhao Wang, Chunfu Qiao, Bing Chen and Limin Jia
J. Mar. Sci. Eng. 2023, 11(7), 1306; https://doi.org/10.3390/jmse11071306 - 27 Jun 2023
Cited by 2 | Viewed by 1174
Abstract
Given the national goal of “emission peaking and carbon neutralization”, China has become the largest country in the world for offshore wind farm construction. At the same time, navigational safety problems in offshore wind farm waters have become increasingly frequent. Owing to the [...] Read more.
Given the national goal of “emission peaking and carbon neutralization”, China has become the largest country in the world for offshore wind farm construction. At the same time, navigational safety problems in offshore wind farm waters have become increasingly frequent. Owing to the complexity of offshore wind farm waters and the small number of accident data samples available for reference, the system theory method is more suitable for selection than the traditional method. Based on causal analysis based on system theory (CAST) and a complex network (CN), in this study, a qualitative and quantitative accident analysis model, CAST-CN, is constructed to analyze a complete case of vessel and wind turbine allision in offshore wind farm waters. The results show that, at the micro level, in addition to the master, crew, shipping company, and typhoon Hato, the maritime safety administration and the wind farm operation management department have a certain impact on the development of the accident discussed in this study. At the macro level, internal and external factors leading to the lack of system safety are identified, and measures and suggestions for system safety improvement are proposed based on analysis. This study can fill the research gap in the systematic analysis of traffic accidents in offshore wind farm waters and provide support for the safety assessment and decision-making of government management departments and research institutes. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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20 pages, 10076 KiB  
Article
Path Planning of an Unmanned Surface Vessel Based on the Improved A-Star and Dynamic Window Method
by Shunan Hu, Shenpeng Tian, Jiansen Zhao and Ruiqi Shen
J. Mar. Sci. Eng. 2023, 11(5), 1060; https://doi.org/10.3390/jmse11051060 - 16 May 2023
Cited by 6 | Viewed by 1726
Abstract
In order to ensure the safe navigation of USVs (unmanned surface vessels) and real-time collision avoidance, this study conducts global and local path planning for USVs in a variable dynamic environment, while local path planning is proposed under the consideration of USV motion [...] Read more.
In order to ensure the safe navigation of USVs (unmanned surface vessels) and real-time collision avoidance, this study conducts global and local path planning for USVs in a variable dynamic environment, while local path planning is proposed under the consideration of USV motion characteristics and COLREGs (International Convention on Regulations for Collision Avoidance at Sea) requirements. First, the basis of collision avoidance decisions based on the dynamic window method is introduced. Second, the knowledge of local collision avoidance theory is used to study the local path planning of USV, and finally, simulation experiments are carried out in different situations and environments containing unknown obstacles. The local path planning experiments with unknown obstacles can prove that the local path planning algorithm proposed in this study has good results and can ensure that the USV makes collision avoidance decisions based on COLREGs when it meets with a ship. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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14 pages, 7442 KiB  
Article
Improved UNet-Based Shoreline Detection Method in Real Time for Unmanned Surface Vehicle
by Jiansen Zhao, Fengchuan Song, Guobao Gong and Shengzheng Wang
J. Mar. Sci. Eng. 2023, 11(5), 1049; https://doi.org/10.3390/jmse11051049 - 15 May 2023
Viewed by 1413
Abstract
Accurate and real-time monitoring of the shoreline through cameras is an invaluable guarantee for the safety of near-shore navigation and berthing of unmanned surface vehicles; existing shoreline detection methods cannot meet both these requirements. Therefore, we propose an improved shoreline detection method to [...] Read more.
Accurate and real-time monitoring of the shoreline through cameras is an invaluable guarantee for the safety of near-shore navigation and berthing of unmanned surface vehicles; existing shoreline detection methods cannot meet both these requirements. Therefore, we propose an improved shoreline detection method to detect shorelines accurately and in real time. We define shoreline detection as the combination of water surface area segmentation and edge detection, the key to which is segmentation. To detect shorelines accurately and in real time, we propose an improved U-Net for water segmentation. This network is based on U-Net, using ResNet-34 as the backbone to enhance the feature extraction capability, with a concise decoder integrated attention mechanism to improve the processing speed while ensuring the accuracy of water surface segmentation. We also introduce transfer learning to improve training efficiency and solve the problem of insufficient data. When obtaining the segmentation result, the Laplace edge detection algorithm is applied to detect the shoreline. Experiments show that our network achieves 97.05% MIoU and 40 FPS with the fewest parameters, which is better than mainstream segmentation networks, and also demonstrate that our shoreline detection method can effectively detect shorelines in real time in various environments. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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29 pages, 16824 KiB  
Article
Modelling, Linearity Analysis and Optimization of an Inductive Angular Displacement Sensor Based on Magnetic Focusing in Ships
by Zhipeng Li, Bonan Wang, Xianbin Wang, Chao Zhang and Xu Meng
J. Mar. Sci. Eng. 2023, 11(5), 1028; https://doi.org/10.3390/jmse11051028 - 11 May 2023
Cited by 2 | Viewed by 1924
Abstract
A sensor for measuring the crankshaft angle of the main engine in ships is designed. Compared with the existing crankshaft angle encoder, this design’s advantage is that there is no need to add a gear system at the free end of the crankshaft, [...] Read more.
A sensor for measuring the crankshaft angle of the main engine in ships is designed. Compared with the existing crankshaft angle encoder, this design’s advantage is that there is no need to add a gear system at the free end of the crankshaft, reducing machining complexity. The purpose of providing high angle resolution over a wide speed range is achieved. Inductive angular displacement sensors (IADSs) require an eddy current magnetic field as a medium to generate the induced voltage. The induced voltage also requires a complex linearization calculation to obtain a linear relationship between angle and voltage. Therefore, a model of the inductive angular displacement sensor based on magnetic focusing (IADSMF) is proposed. Magnetic focusing is introduced into the IADS to replace the eddy current magnetic field with a focusing magnetic field. The main disadvantage of traditional IADSs, which is that they cannot reduce the eddy current magnetic field, is mitigated. An approximate square–shaped focusing magnetic field (12.4 × 12.4 mm2) is formed using the magnetic field constraint of the magnetic conductor. When the receiving coil undergoes a position change relative to the square–shaped focusing magnetic field, the voltage generated via the receiving coil is measured using the electromagnetic induction principle to achieve angular displacement measurement. A mathematical model of the IADSMF is derived. Induced voltages at different frequencies and rotational speeds are simulated and analyzed via MATLAB. The results show that frequency is the main factor affecting the induced voltage amplitude. The sensitivity of the IADSMF is 0.2023 mV/°. The resolution and measurement of the IADSMF range from 0.06° and 0–360°. Compared with a conventional planar coil–based IADS, the eddy current loss is reduced from 2.1304 to 0.3625 W. Direct linearization of the angular displacement with the induced voltage is achieved through designing a square–shaped focusing field and receiving coil. After optimizing the sensor structure with the optimization algorithm, the linearity error is 0.6012%. Finally, this sensor provides a theoretical basis and research ideas for IADS development in ships and navigation. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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18 pages, 3833 KiB  
Article
An Improved NSGA-II Based on Multi-Task Optimization for Multi-UAV Maritime Search and Rescue under Severe Weather
by Yue Ma, Bo Li, Wentao Huang and Qinqin Fan
J. Mar. Sci. Eng. 2023, 11(4), 781; https://doi.org/10.3390/jmse11040781 - 04 Apr 2023
Cited by 3 | Viewed by 1529
Abstract
The international trade heavily relies on maritime transportation. Due to the vastness of the ocean, once an accident happens, fast maritime search and rescue (MSR) is a must, as it is of life-and-death matter. Using unmanned air vehicles (UAVs) is an effective approach [...] Read more.
The international trade heavily relies on maritime transportation. Due to the vastness of the ocean, once an accident happens, fast maritime search and rescue (MSR) is a must, as it is of life-and-death matter. Using unmanned air vehicles (UAVs) is an effective approach to completing complex MSR tasks, especially when the environment is dangerous and changeable. However, how to effectively plan paths for multi-UAVs under severe weather, e.g., to rescue the most urgent targets in the shortest time, is a challenging task. In this study, an improved NSGA-II based on multi-task optimization (INSGA-II-MTO) is proposed to plan paths for multi-UAVs in the MSR tasks. In the INSGA-II-MTO, a novel population initialization method is proposed to improve the diversity of an initial population. Further, two tasks are introduced during the execution of the search algorithm. Namely, one assistant task, which solves a simplified MSR problem through multi-task optimization, is implemented to provide necessary evolutional knowledge to a main task that solves an original MSR problem. The performance of the proposed INSGA-II-MTO is compared with other competitors in three MSR scenarios. Experimental results indicate that the proposed algorithm performs best among the compared ones. It is observed that the INSGA-II-MTO can find a set of shorter total paths and handle the most urgent task in the shortest possible time. Therefore, the proposed method is an effective and promising approach to solving multi-UAVs MSR problems to reduce human causalities and property losses. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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19 pages, 3508 KiB  
Article
A Study on the Correlation between Ship Movement Characteristics and Ice Conditions in Polar Waters
by Liang Chen, Changhai Huang and Yanhao Wang
J. Mar. Sci. Eng. 2023, 11(4), 729; https://doi.org/10.3390/jmse11040729 - 27 Mar 2023
Viewed by 1236
Abstract
The opening of arctic routes provides a new option for international navigation ships. The correlation between ship movement characteristics and ice conditions should be known, which will help ships adapt to the polar waters. Based on the voyage data and sea ice manual [...] Read more.
The opening of arctic routes provides a new option for international navigation ships. The correlation between ship movement characteristics and ice conditions should be known, which will help ships adapt to the polar waters. Based on the voyage data and sea ice manual observation data of the ‘XUE LONG’ ship’s six voyages in polar waters, a correlation analysis model of ice conditions and ship movement characteristics was established in this work. First, the ship movement characteristics in polar waters were analyzed, such as the distribution characteristics of ship speeds, courses, and variation characteristics by using the descriptive statistical analysis method and data visualization analysis method. Then, by using multivariate correlation analysis and univariate controlled correlation analysis methods, the correlation between movement characteristics and ice conditions, such as ice concentration and thickness, and the correlation between different ice conditions themselves, were quantitatively analyzed. The result shows that the correlation analysis model of ice conditions and ship movement characteristics is reliable and effective and can obtain quantitative correlation analysis results. On the one hand, sea ice thickness has almost no significant correlation with ship movement characteristics, excluding the influence of sea ice concentration. On the other hand, excluding the influence of sea ice thickness, sea ice concentration is still significantly correlated with the absolute value of speed, speed variation, and course variation. The conclusions of this work have important reference significance for polar scientific investigations, commercial ships’ voyages in icy waters, and ships’ designs for icy waters. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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26 pages, 6068 KiB  
Article
Research on Position Sensorless Control of RDT Motor Based on Improved SMO with Continuous Hyperbolic Tangent Function and Improved Feedforward PLL
by Hongfen Bai, Bo Yu and Wei Gu
J. Mar. Sci. Eng. 2023, 11(3), 642; https://doi.org/10.3390/jmse11030642 - 17 Mar 2023
Cited by 15 | Viewed by 1433
Abstract
With the increasing use of electric propulsion ships, the emergence of the shaftless rim-driven thruster (RDT) as a revolutionary integrated motor thruster is gradually becoming an important development direction for green ships. The shaftless structure of RDTs leads to their dependence on position [...] Read more.
With the increasing use of electric propulsion ships, the emergence of the shaftless rim-driven thruster (RDT) as a revolutionary integrated motor thruster is gradually becoming an important development direction for green ships. The shaftless structure of RDTs leads to their dependence on position sensorless control techniques. In this study, a novel control algorithm using a composite sliding mode observer (SMO) with a modified feed-forward phase-locked loop (PLL) is presented for achieving high accuracy position and speed control of shaftless RDT motors. The deviation between the observed and actual currents is exploited to develop a current SMO to extract back electromotive force (back-EMF) errors. On this basis, a back-EMF observer is established to achieve accurate estimation of the back-EMF. The basic structure of the PLL was modified and incorporates a speed feedforward mechanism, which enhances the performance of rotor position estimation and facilitates bidirectional rotation. The stability of the algorithm has been verified in Matlab/Simulink for a range of steady-state, dynamic, and ship propeller loading conditions. Remarkably, the control algorithm boasts an impressive adjustment time of approximately 0.006 s and its position estimation error may be as low as 0.03 rad. Simulation results highlight the performance of the algorithm to achieve bidirectional rotation, while exhibiting fast convergence, minimal vibration, exceptional control accuracy, and robustness. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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13 pages, 6105 KiB  
Article
A Novel Intelligent Ship Detection Method Based on Attention Mechanism Feature Enhancement
by Yingdong Ye, Rong Zhen, Zheping Shao, Jiacai Pan and Yubing Lin
J. Mar. Sci. Eng. 2023, 11(3), 625; https://doi.org/10.3390/jmse11030625 - 16 Mar 2023
Cited by 4 | Viewed by 1400
Abstract
The intelligent perception ability of the close-range navigation environment is the basis of autonomous decision-making and control of unmanned ships. In order to realize real-time perception of the close-range environment of unmanned ships, an enhanced attention mechanism YOLOv4 (EA-YOLOv4) algorithm is proposed. First [...] Read more.
The intelligent perception ability of the close-range navigation environment is the basis of autonomous decision-making and control of unmanned ships. In order to realize real-time perception of the close-range environment of unmanned ships, an enhanced attention mechanism YOLOv4 (EA-YOLOv4) algorithm is proposed. First of all, on the basis of YOLOv4, the convolutional block attention module (CBAM) is used to search for features in channel and space dimensions, respectively, to improve the model’s feature perception of ship targets. Then, the improved-efficient intersection over union (EIoU) loss function is used to replace the complete intersection over union (CIoU) loss function of the YOLOv4 algorithm to improve the algorithm’s perception of ships of different sizes. Finally, in the post-processing of algorithm prediction, soft non-maximum suppression (Soft-NMS) is used to replace the non-maximum suppression (NMS) of YOLOv4 to reduce the missed detection of overlapping ships without affecting the efficiency. The proposed method is verified on the large data set SeaShips, and the average accuracy rate of mAP0.5–0.95 reaches 72.5%, which is 10.7% higher than the original network YOLOv4, and the FPS is 38 frames/s, which effectively improves the ship detection accuracy while ensuring real-time performance. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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27 pages, 6865 KiB  
Article
A Refined Collaborative Scheduling Method for Multi-Equipment at U-Shaped Automated Container Terminals Based on Rail Crane Process Optimization
by Yongsheng Yang, Shu Sun, Meisu Zhong, Junkai Feng, Furong Wen and Haitao Song
J. Mar. Sci. Eng. 2023, 11(3), 605; https://doi.org/10.3390/jmse11030605 - 13 Mar 2023
Cited by 1 | Viewed by 1638
Abstract
A U-shaped automated container terminal (ACT) has been proposed for the first time globally and has been adopted to construct the Beibu Gulf Port ACT. In this ACT layout, the double cantilevered rail crane (DCRC) simultaneously provides loading and unloading services for the [...] Read more.
A U-shaped automated container terminal (ACT) has been proposed for the first time globally and has been adopted to construct the Beibu Gulf Port ACT. In this ACT layout, the double cantilevered rail crane (DCRC) simultaneously provides loading and unloading services for the external container trucks (ECTs) and the automatic guided vehicles (AGVs) entering the yard. The DCRC has a complex scheduling coupling relationship with the AGV and the ECT, and its mathematical model is extremely complex. There is an urgent need to study a practical collaborative scheduling optimization model and algorithm for the DCRC, the AGV, and the ECT. In this paper, we optimize the process flow of DCRCs to study the refined collaborative scheduling model of DCRCs, AGVs and ECTs in U-shaped ACTs. Firstly, we analyze the operation process of the DCRC and divide the 16 loading and unloading conditions of the DCRC into four operation modes for process optimization. Secondly, different variables and parameters are set for the DCRC’s four operating modes, and a refined collaborative dispatching model for the DCRCs with AGVs and ECTs is proposed. Finally, a practical adaptive co-evolutionary genetic algorithm solves the model. Meanwhile, arithmetic examples verify the correctness and practicality of the model and algorithm. The experimental results show that the total running time of the DCRCs is the shortest in the U-shaped ACT when the number of quay cranes (QC) to DCRC and AGV ratios are 1:2 and 1:10, respectively. At the same time, the number of QCs and DCRCs has a more significant impact on the efficiency of the ACT than that of AGVs, and priority should be given to the allocation of QCs and DCRCs. The research results have essential guidance value for U-shaped ACTs under construction and enrich the theory and method of collaborative scheduling of U-shaped ACT equipment. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Maritime Transportation)
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