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Artificial Intelligence-Driven Ocean Monitoring (AID-OM)

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 19592

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Special Issue Editors

Prof. Dr. Jiachen Yang

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Guest Editor

School of Electrical and Information Engineering, Tianjin University, Tianjin 300000, China
Interests: smart ocean system; intelligent monitoring; sensing network; Internet of Things; marine information processing; vision sensors
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Wen Lu

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Guest Editor

School of Electronic Engineering, Xidian University, Xi’an 710126, China
Interests: physical imaging systems; cloud platform; Internet of Things; vision sensors

Dr. Yun Liu

E-Mail Website
Guest Editor

College of Information, Liaoning University, Shenyang 110136, China
Interests: underwater image processing; artificial intelligence; vision sensors; marine information processing

Special Issue Information

Dear Colleagues,

With the continuous expansion of human activities, the ocean has become an important area for trade development and resource development. In recent years, with the development of technologies such as the multi-device collaboration, Internet-of-Things, and space monitoring, a variety of maritime monitoring platforms (e.g. vessels, satellites, autonomous underwater vehicle, shore-based platforms, etc) have produced a large amount of data and multi-category information. How to effectively collect, transmit, store and analyze marine big data effectively has become a research hotspot.

Marine monitoring relies on the integration of ocean sensing network and information processing technology, and uses wireless communication technology and intelligent control scheme to build a safe, efficient and comprehensive marine monitoring system. It includes multiple application services such as marine navigation, environmental intelligence monitoring, resource development, and disaster warning, etc.

Intelligent monitoring is the nerve system that operates the ocean, and it is also an important part of the intelligent industry 4.0. It uses a combination of three methods: comprehensive sensing, real-time transmission of the Internet, and knowledge mining of big data analysis to form a new intelligent system for sharing resources and collaborating activities. Based on the Internet of Things, emerging technologies such as big data, cloud computing, and artificial intelligence make ocean activities more intelligent, green and safe.

This Special Section aims to provide researchers and practitioners a platform to present innovative solutions based on Artificial Intelligence Technologies. The focus of this Special Issue is to address the current research challenges by encouraging submissions related to the advanced Artificial Intelligence Technologies in Smart Ocean System.Topics of Interest include but not limited to the following areas:

Undersea sensors and communication technology of marine monitoring
Remote sensing information processing and pattern recognition of marine monitoring
Maritime multi-device collaboration and interaction
Edge computing and information processing technology of marine monitoring
Ocean of Things systems optimization and design of marine monitoring
Establishment of marine environment monitoring systems
Marine big data intelligent mining and analysis technology
Environmental disaster warning of marine monitoring
Development of intelligent navigation systems of marine monitoring
Security of maritime information networks
Information visualization and applications of marine monitoring

Prof. Dr. Jiachen Yang
Prof. Dr. Wen Lu
Dr. Yun Liu
Guest Editors

Manuscript Submission Information

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Keywords

smart ocean system
intelligent monitoring
marine monitoring
wireless communication technology
artificial intelligence technologies
marine big data
edge computing

Published Papers (7 papers)

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Research

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23 pages, 39626 KiB

Open AccessArticle

A Hybrid Rule-Based and Data-Driven Approach to Illegal Transshipment Identification with Interpretable Behavior Features

by Lei Deng, Yuchen Niu, Limin Jia, Wen Liu and Yu Zang

Sensors 2022, 22(24), 9581; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249581 - 07 Dec 2022

Cited by 1 | Viewed by 1154

Abstract

Illegal transshipment of maritime ships is usually closely related to illegal activities such as smuggling, human trafficking, piracy plunder, and illegal fishing. Intelligent identification of illegal transshipment has become an important technical means to ensure the safety of maritime transport. However, due to different geographical environments, legal policies and regulatory requirements in each sea area, there are differences in the movement characteristics and geographical distribution of illegal transshipment behavior in different time and space. Moreover, in areas with dense traffic flow, normal navigation behavior can easily be identified as illegal transshipment, resulting in a high rate of misidentification. This paper proposes a hybrid rule-based and data-driven approach to solve the problem of missing identification in fixed threshold methods and introduces a traffic density feature to reduce the misidentification rate in dense traffic areas. The method is both interpretable and adaptable through unsupervised clustering to get suitable threshold distribution combination for regulatory sea areas. The evaluation results in two different sea areas show that the proposed method is applicable. Compared with other widely used identification methods, this method identifies more illegal transshipment events, which are highly suspicious, and gives warning much earlier. The proposed method can even filter out misidentification events from compared methods’ results, which account for more than half of the total number. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Ocean Monitoring (AID-OM))

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21 pages, 17541 KiB

Open AccessArticle

A Semi-Supervised Methodology for Fishing Activity Detection Using the Geometry behind the Trajectory of Multiple Vessels

by Martha Dais Ferreira, Gabriel Spadon, Amilcar Soares and Stan Matwin

Sensors 2022, 22(16), 6063; https://0-doi-org.brum.beds.ac.uk/10.3390/s22166063 - 13 Aug 2022

Cited by 7 | Viewed by 2149

Abstract

Automatic Identification System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data play a significant role in tracking vessel activity and mapping mobility patterns such as those found during fishing activities. Accordingly, this paper proposes a geometric-driven semi-supervised approach for fishing activity detection from AIS data. Through the proposed methodology, it is shown how to explore the information included in the messages to extract features describing the geometry of the vessel route. To this end, we leverage the unsupervised nature of cluster analysis to label the trajectory geometry, highlighting changes in the vessel’s moving pattern, which tends to indicate fishing activity. The labels obtained by the proposed unsupervised approach are used to detect fishing activities, which we approach as a time-series classification task. We propose a solution using recurrent neural networks on AIS data streams with roughly 87% of the overall F-score on the whole trajectories of 50 different unseen fishing vessels. Such results are accompanied by a broad benchmark study assessing the performance of different Recurrent Neural Network (RNN) architectures. In conclusion, this work contributes by proposing a thorough process that includes data preparation, labeling, data modeling, and model validation. Therefore, we present a novel solution for mobility pattern detection that relies upon unfolding the geometry observed in the trajectory. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Ocean Monitoring (AID-OM))

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24 pages, 7127 KiB

Open AccessArticle

Depth-Keeping Control for a Deep-Sea Self-Holding Intelligent Buoy System Based on Inversion Time Constraint Stability Strategy Optimization

by Qiang Wang, Xingfei Li, Zurong Qiu, Shizhong Yang, Wei Zhou and Jingbo Zhao

Sensors 2022, 22(3), 1096; https://0-doi-org.brum.beds.ac.uk/10.3390/s22031096 - 31 Jan 2022

Cited by 1 | Viewed by 2132

Abstract

Based on the nonlinear disturbance observer (NDO), the inversion time-constraint stability strategy (ITCS) is designed to make the deep-sea self-holding intelligent buoy (DSIB) system hovered at an appointed depth within a specified time limit. However, it is very challenging to determine the optimal parameters of an ITCS depth controller. Firstly, a genetic algorithm based on quantum theory (QGA) is proposed to obtain the optimal parameter combination by using the individual expression form of quantum bit and the adjustment strategy of quantum rotary gate. To improve the speed and accuracy of global search in the QGA optimization process, taking the number of odd and even evolutions as the best combination point of the genetic and chaos particle swarm algorithm (GACPSO), an ITCS depth controller based on GACPSO strategy is proposed. Besides, the simulations and hardware-in-the-loop system experiments are conducted to examine the effectiveness and feasibility of the proposed QGA–ITCS and GACPSO–ITCS depth controller. The results show that the proposed GACPSO–ITCS depth controller provides higher stability with smaller steady-state error and less settling time in the depth-control process. The research of the proposed method can provide a stable operation condition for the marine sensors carried by the DSIB. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Ocean Monitoring (AID-OM))

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18 pages, 5321 KiB

Open AccessArticle

The Sea Route Planning for Survey Vessel Intelligently Navigating to the Survey Lines

by Jiachen Yang, Tianlei Ni, Lin Liu, Jiabao Wen, Jingyi He and Zhengjian Li

Sensors 2022, 22(2), 482; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020482 - 09 Jan 2022

Cited by 3 | Viewed by 2629

Abstract

Marine surveying is an important part of marine environment monitoring systems. In order to improve the accuracy of marine surveying and reduce investment in artificial stations, it is necessary to use high-precision GNSS for shipborne navigation measurements. The basic measurement is based on the survey lines that are already planned by surveyors. In response to the needs of survey vessels sailing to the survey line, a method framework for the shortest route planning is proposed. Then an intelligent navigation system for survey vessels is established, which can be applied to online navigation of survey vessels. The essence of the framework is that the vessel can travel along the shortest route to the designated survey line under the limitation of its own minimum turning radius. Comparison and analysis of experiments show that the framework achieves better optimization. The experimental results show that our proposed method can enable the vessel to sail along a shorter path and reach the starting point of the survey line at the specified angle. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Ocean Monitoring (AID-OM))

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17 pages, 33299 KiB

Open AccessArticle

A Marine Organism Detection Framework Based on the Joint Optimization of Image Enhancement and Object Detection

by Xueting Zhang, Xiaohai Fang, Mian Pan, Luhua Yuan, Yaxin Zhang, Mengyi Yuan, Shuaishuai Lv and Haibin Yu

Sensors 2021, 21(21), 7205; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217205 - 29 Oct 2021

Cited by 16 | Viewed by 2191

Abstract

Underwater vision-based detection plays an increasingly important role in underwater security, ocean exploration and other fields. Due to the absorption and scattering effects of water on light, as well as the movement of the carrier, underwater images generally have problems such as noise pollution, color cast and motion blur, which seriously affect the performance of underwater vision-based detection. To address these problems, this study proposes an end-to-end marine organism detection framework that can jointly optimize the image enhancement and object detection. The framework uses a two-stage detection network with dynamic intersection over union (IoU) threshold as the backbone and adds an underwater image enhancement module (UIEM) composed of denoising, color correction and deblurring sub-modules to greatly improve the framework’s ability to deal with severely degraded underwater images. Meanwhile, a self-built dataset is introduced to pre-train the UIEM, so that the training of the entire framework can be performed end-to-end. The experimental results show that compared with the existing end-to-end models applied to marine organism detection, the detection precision of the proposed framework can improve by at least 6%, and the detection speed has not been significantly reduced, so that it can complete the high-precision real-time detection of marine organisms. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Ocean Monitoring (AID-OM))

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26 pages, 11533 KiB

Open AccessArticle

Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data

by Donghyun Kim, Gian Antariksa, Melia Putri Handayani, Sangbong Lee and Jihwan Lee

Sensors 2021, 21(15), 5200; https://0-doi-org.brum.beds.ac.uk/10.3390/s21155200 - 31 Jul 2021

Cited by 21 | Viewed by 4936

Abstract

In this study, we proposed a data-driven approach to the condition monitoring of the marine engine. Although several unsupervised methods in the maritime industry have existed, the common limitation was the interpretation of the anomaly; they do not explain why the model classifies specific data instances as an anomaly. This study combines explainable AI techniques with anomaly detection algorithm to overcome the limitation above. As an explainable AI method, this study adopts Shapley Additive exPlanations (SHAP), which is theoretically solid and compatible with any kind of machine learning algorithm. SHAP enables us to measure the marginal contribution of each sensor variable to an anomaly. Thus, one can easily specify which sensor is responsible for the specific anomaly. To illustrate our framework, the actual sensor stream obtained from the cargo vessel collected over 10 months was analyzed. In this analysis, we performed hierarchical clustering analysis with transformed SHAP values to interpret and group common anomaly patterns. We showed that anomaly interpretation and segmentation using SHAP value provides more useful interpretation compared to the case without using SHAP value. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Ocean Monitoring (AID-OM))

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Other

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9 pages, 3020 KiB

Open AccessPerspective

Confronting Deep-Learning and Biodiversity Challenges for Automatic Video-Monitoring of Marine Ecosystems

by Sébastien Villon, Corina Iovan, Morgan Mangeas and Laurent Vigliola

Sensors 2022, 22(2), 497; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020497 - 10 Jan 2022

Cited by 10 | Viewed by 2993

Abstract

With the availability of low-cost and efficient digital cameras, ecologists can now survey the world’s biodiversity through image sensors, especially in the previously rather inaccessible marine realm. However, the data rapidly accumulates, and ecologists face a data processing bottleneck. While computer vision has long been used as a tool to speed up image processing, it is only since the breakthrough of deep learning (DL) algorithms that the revolution in the automatic assessment of biodiversity by video recording can be considered. However, current applications of DL models to biodiversity monitoring do not consider some universal rules of biodiversity, especially rules on the distribution of species abundance, species rarity and ecosystem openness. Yet, these rules imply three issues for deep learning applications: the imbalance of long-tail datasets biases the training of DL models; scarce data greatly lessens the performances of DL models for classes with few data. Finally, the open-world issue implies that objects that are absent from the training dataset are incorrectly classified in the application dataset. Promising solutions to these issues are discussed, including data augmentation, data generation, cross-entropy modification, few-shot learning and open set recognition. At a time when biodiversity faces the immense challenges of climate change and the Anthropocene defaunation, stronger collaboration between computer scientists and ecologists is urgently needed to unlock the automatic monitoring of biodiversity. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Ocean Monitoring (AID-OM))

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