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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.

With the vast majority of global trade volume and value reliant on maritime transport, accurate prediction of vessel estimated time of arrival (ETA) is crucial for optimizing supply chain efficiency and managing logistical complexities in port operations. This review paper systematically examines the current state of research and practices in the field of vessel ETA prediction, highlighting significant trends, methodologies, and technologies. It explores various approaches, including classical methods, machine learning and deep learning algorithms, and hybrid methods, developed to enhance the accuracy and reliability of vessel travel time and arrival time predictions. Additionally, this paper categorizes key influencing factors and metrics, and identifies open issues and challenges within current prediction models. Concluding with proposed future research directions aimed at addressing the identified gaps and leveraging technological advancements, this review emphasizes the importance of fostering innovation in maritime ETA prediction systems, particularly within the framework of Intelligent Transportation Systems (ITSs) and maritime logistics. By applying a systematic literature review (SLR) methodology and conducting an in-depth evaluation, the results provide a comprehensive overview of vessel ETA prediction for researchers, practitioners, and policy makers involved in maritime transport and logistics, and offer insights into the potential for improved efficiency, safety, and environmental sustainability in waterway networks.

We present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping management, and other intelligent transportation system applications in the future. The fundamental task of maritime transportation surveillance and autonomous ship navigation is to construct a reachable visual perception system that requires high efficiency and high accuracy of marine object detection. Therefore, high-performance deep learning-based algorithms and high-quality marine-related datasets need to be summarized. This survey focuses on summarizing the methods and application scenarios of maritime object detection, analyzes the characteristics of different marine-related datasets, highlights the marine detection application of the YOLO series model, and also discusses the current limitations of object detection based on deep learning and possible breakthrough directions. The large-scale, multiscenario industrialized neural network training is an indispensable link to solve the practical application of marine object detection. A widely accepted and standardized large-scale marine object verification dataset should be proposed.

Forecasting vessel flows is important to the development of intelligent transportation systems in the maritime field, as real-time and accurate traffic information has favorable potential in helping a maritime authority to alleviate congestion, mitigate emission of GHG (greenhouse gases) and enhance public safety, as well as assisting individual vessel users to plan better routes and reduce additional costs due to delays. In this paper, we propose three deep learning-based solutions to forecast the inflow and outflow of vessels within a given region, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and the integration of a bidirectional LSTM network with a CNN (BDLSTM-CNN). To apply those solutions, we first divide the given maritime region into M × N grids, then we forecast the inflow and outflow for all the grids. Experimental results based on the real AIS (Automatic Identification System) data of marine vessels in Singapore demonstrate that the three deep learning-based solutions significantly outperform the conventional method in terms of mean absolute error and root mean square error, with the performance of the BDLSTM-CNN-based hybrid solution being the best.

With rapid technological innovations spurred by the Fourth Industrial Revolution (Industry 4.0), today’s seaports are pressured to transform the way they operate in order to handle traffic flows. Such a transformation calls for the development of a smart port system. Despite the growing interest in smart ports, their underlying framework, architecture, and potential ramifications for port productivity are not well documented in the maritime logistics literature. To help port communities better comprehend smart port concepts and successfully develop a smart port within a global supply chain, this paper synthesizes core smart port concepts, designs underlying architecture, and proposes specific milestones for monitoring the smart port development project. We use content analysis and then we identify key success factors (e.g., essential components for the smart port architecture, value propositions, smart port performance metrics) for the establishment and sustainable growth of the smart port. The paper also aims to provide practical guidance for dealing with smart port challenges and opportunities. Our research reveals that a smart port reduces port-user response time, improves port asset utilization, and enhances maritime logistics visibility by automating and integrating end-to-end port operations digitally without human intervention.

At present, with the development of waterborne transport vehicles, research on ship faces a new round of challenges in terms of intelligence and autonomy. The concept of maritime autonomous surface ships (MASS) has been put forward by the International Maritime Organization in 2017, in which MASS become the new focus of the waterborne transportation industry. This paper elaborates on the state-of-the-art research on motion control of MASS. Firstly, the characteristics and current research status of unmanned surface vessels in MASS and conventional ships are summarized, and the system composition of MASS is analyzed. In order to better realize the self-adaptability of the MASS motion control, the theory and algorithm of ship motion control-related systems are emphatically analyzed under the condition of classifying ship motion control. Especially, the application of intelligent algorithms in the ship control field is summarized and analyzed. Finally, this paper summarizes the challenges faced by MASS in the model establishment, motion control algorithms, and real ship experiments, and proposes the composition of MASS motion control system based on variable autonomous control strategy. Future researches on the accuracy and diversity of developments and applications to MASS motion control are suggested.

With the continuous advancement of intelligent technology, intelligent transportation has become a prominent area of research within the transportation sector. In contrast to road-based intelligent transportation systems, the development of intelligent maritime transportation remains in its early stages. This paper proposes a foundational theory and related concepts for ship navigation guidance to address the gap in theoretical support for intelligent maritime transportation. By examining both individual and system-level attributes, the similarities and differences between vehicles and ships are analyzed. Drawing on the theoretical framework of road-based intelligent transportation and the unique characteristics of maritime transportation, the paper introduces the core theory of intelligent maritime transportation, specifically the theoretical structure of a ship’s intelligent guidance system. Based on this theoretical framework, a prototype of the ship navigation intelligent guidance system was developed and tested. The experimental results demonstrate that the system successfully integrates core technologies, each fulfilling its intended role. The system significantly enhances the safety of ship navigation and fosters the advancement of intelligent maritime transportation. Therefore, the proposed definition of ship navigation guidance and the associated technical framework for the intelligent guidance system not only enrich the theoretical foundations of intelligent maritime transportation but also offer valuable insights for the development of intelligent ships.

Maritime traffic pattern recognition plays a major role in intelligent transportation services, ship monitoring, route planning, and other fields. Facilitated by the establishment of terrestrial networks and satellite constellations of the automatic identification system (AIS), large quantities of spatial and temporal information make ships’ paths trackable and are useful in maritime traffic pattern research. The maritime traffic pattern may vary with changes in the traffic environment, so the recognition method of the maritime traffic pattern should be adaptable to changes in the traffic environment. To achieve this goal, a dynamic maritime traffic pattern recognition method is presented using AIS data, which are cleaned, compressed, partitioned, and clustered online. Old patterns are removed as expired trajectories are deleted, and new patterns are created as new trajectories are added. This method is suitable for processing massive stream data. Experiments show that when the marine traffic route changes due to the navigation environment, the maritime traffic pattern adjusts automatically.

The rapid advancement of maritime technologies, particularly in the domain of autonomous ships and maritime intelligent transport systems (MITS), necessitates a robust framework for digital interoperability at both national and international levels. This paper presents an in-depth analysis of the current state of standardization within MITS. It emphasizes the importance of harmonizing efforts to facilitate the integration of innovative technologies into the maritime sector, ensuring safety, efficiency, and environmental sustainability. To gain broader insights, standardization practices from other industries are examined. In conclusion, the paper argues that enhancing collaboration and standardization in MITS is essential for the successful implementation of maritime innovations. It calls for a collective effort from national and international stakeholders to develop a unified framework that supports the growth of autonomous ship technology and the evolution of intelligent maritime transport systems.

New Technologies, Artificial Intelligence and Shipping Law in the 21st Century consists of edited versions of the papers delivered at the Institute of International Shipping and Trade Law’s 14th International Colloquium at Swansea Law School in September 2018. Written by a combination of top academics and highly experienced legal practitioners, these papers have been carefully co-ordinated to give the reader a first-class insight into the issues surrounding new technology and shipping. The book is set out in three parts: Part I offers a detailed and critical analysis of issues that are emerging, and those that are likely to emerge, from the use of advanced computer technology, particularly at the contracting process and in the context of issuing trading documents. Part 2 focusses on artificial intelligence and discusses the contemporary issues that will emerge once autonomous ships and similar crafts are put to use in the world’s oceans. As well as this, the legal impact of ports utilising artificial intelligence and computer technology will also be considered. Part 3 analyses how the increasing use of legal technology is changing insurance underwriting and shipping litigation. An invaluable guide to the recent technological advances in shipping, this book is vital reading for both professional and academic readers. Table of cases. Table of legislation. Notes on editors and contributors. Foreword. Preface. Part 1 Effect of New Technologies on Contracting in Shipping Practice. Chapter 1 Blockchain and Smart Contracts in Shipping and Transport: A Legal Revolution is About to Arrive? Francesco Munari. Chapter 2 Smart Contracts - The BIMCO Experience Grant Hunter. Chapter 3 Can Commercial Law Accommodate New Technologies in International Shipping? Michael Sturley. Chapter 4 Electronic Signature in Shipping Practice Erik Røsæg. Chapter 5 Pinning Down Delivery: Glencore v. MSC and the Use of PIN Codes to Effect Delivery Simon Rainey. Part 2 Artificial Intelligence and Shipping. Chapter 6 Autonomous Shipping and Maritime Law Paul Dean and Henry Clack. Chapter 7 BOTPORT Law - The regulatory Agenda for the Transition to Smart Ports Erik van Hoydonk. Chapter 8 Autonomous Vessels and Third Party Liabilities - The Elephant in the Room Barış Soyer. Chapter 9 Shipping - Product Liability Goes High-Tech Andrew Tettenborn. Chapter 10 Who is the Master Now? Regulatory and Contractual Challenges of Unmanned Vessels Simon Baughen. Chapter 11 Carrier Liability for Unmanned Ships. Goodbye Crew, Hello Liability Frank Stevens. Part 3 Legal Tech and Its Impact on Shipping and Insurance. Chapter 12 Impact of Technology on Disclosure in Shipping Litigation Peter MacDonald-Eggers. Chapter 13 Insurance and Artificial Intelligence: Underwriting, Claims and Litigation Simon Cooper. Index. Barış Soyer is Professor of Commercial and Maritime Law and Director of the Institute of International Shipping and Trade Law, Law School, Swansea University. He was previously Lecturer in Law, University of Exeter (2000–2001). Andrew Tettenborn is Professor of Commercial Law at the Institute of International Shipping and Trade Law, Law School, University of Swansea. He was previously Professor of Law, University of Exeter (1996–2010) and Fellow of Pembroke College and Lecturer in Law, University of Cambridge (1979–1996).