An efficient model for small object detection in the maritime environment

Applied Ocean Research, Год журнала: 2024, Номер 152, С. 104194 - 104194

Опубликована: Авг. 27, 2024

Язык: Английский

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Robust optimization for a class of ship traffic scheduling problem with uncertain arrival and departure times

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108257 - 108257

Опубликована: Апрель 2, 2024

Язык: Английский

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A robust method for multi object tracking in autonomous ship navigation systems

Ocean Engineering, Год журнала: 2024, Номер 311, С. 118560 - 118560

Опубликована: Июль 31, 2024

A novel Maritime Multi-Object Tracking method is proposed, combining a deep learning-based object detector with target association algorithms to achieve robust sea-surface multi-object tracking. Specifically, the employs You Only Look Once version 7 for detection. In data part, module onboard camera motion compensation developed, maritime dynamic spatial information-based intersection-over-union presented as similarity metric, and progressive refinement cascade matching strategy designed enhance tracker's tracking capabilities. The Jari Dataset utilised validate effectiveness performance of proposed method. Experimental results demonstrate that compared earlier process, exhibits significant enhancement in multiple accuracy, an increase 27.8% achieved score 81.3. particular, it reduces number identifications switching missed targets, achieving holistically preferable performance. Meanwhile, speed fulfils engineering application requirements autonomous ship navigation system.

Язык: Английский

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Dynamic collision avoidance for maritime autonomous surface ships based on deep Q-network with velocity obstacle method

Ocean Engineering, Год журнала: 2025, Номер 320, С. 120335 - 120335

Опубликована: Янв. 13, 2025

Язык: Английский

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Estimating emissions from fishing vessels: a big Beidou data analytical approach

Frontiers in Marine Science, Год журнала: 2024, Номер 11

Опубликована: Июль 9, 2024

Fishing vessels are important contributors to global emissions in terms of greenhouse gases and air pollutants. However, few studies have addressed the from fishing on grounds. In this study, a framework for estimating vessel emissions, using bottom-up dynamic method based big data Beidou VMS (vessel monitoring system) vessels, is proposed applied survey East China Sea. The results study established one-year emission inventory This was first use estimate area, will help support management their carbon emissions.

Язык: Английский

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A machine learning-based adaptive heuristic for vessel scheduling problem under uncertainty via chance-constrained programming

Computers & Electrical Engineering, Год журнала: 2024, Номер 119, С. 109523 - 109523

Опубликована: Авг. 9, 2024

Язык: Английский

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Multiple ships cooperative navigation and collision avoidance using multi-agent reinforcement learning with communication

Ocean Engineering, Год журнала: 2025, Номер 320, С. 120244 - 120244

Опубликована: Янв. 14, 2025

Язык: Английский

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A collision avoidance decision-making method for multiple marine autonomous surface ships based on P3DL-PPO algorithm

Journal of Marine Science and Technology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 15, 2025

Язык: Английский

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Identification of ship motion model based on IPPSA-MTCN-MHSA

Journal of Computational Science, Год журнала: 2025, Номер unknown, С. 102572 - 102572

Опубликована: Март 1, 2025

Язык: Английский

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Machine Learning in Maritime Safety for Autonomous Shipping: A Bibliometric Review and Future Trends

Journal of Marine Science and Engineering, Год журнала: 2025, Номер 13(4), С. 746 - 746

Опубликована: Апрель 8, 2025

Autonomous vessels are becoming paramount to ocean transportation, while they also face complex risks in dynamic marine environments. Machine learning plays a crucial role enhancing maritime safety by leveraging its data analysis and predictive capabilities. However, there has been no review grounded bibliometric this field. To explore the research evolution knowledge frontier field of for autonomous shipping, was conducted using 719 publications from Web Science database, covering period 2000 up May 2024. This study utilized VOSviewer, alongside traditional literature methods, construct network map perform cluster analysis, thereby identifying hotspots, trends, emerging frontiers. The findings reveal robust cooperative among journals, researchers, institutions, countries or regions, underscoring interdisciplinary nature domain. Through review, we found that machine methods evolving toward systematic comprehensive direction, integration with AI human interaction may be next bellwether. Future will concentrate on three main areas: objectives towards proactive management coordination, developing advanced technologies, such as bio-inspired sensors, quantum learning, self-healing systems, decision-making algorithms generative adversarial networks (GANs), hierarchical reinforcement (HRL), federated learning. By visualizing collaborative networks, analyzing evolutionary lays groundwork pioneering advancements sets visionary angle future shipping. Moreover, it facilitates partnerships between industry academia, making concerted efforts domain USVs.

Язык: Английский

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