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Underwater Observation Technology in Marine Environment
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Underwater Observation Technology in Marine Environment

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: 20 July 2025 | Viewed by 4275

Special Issue Editors

Dr. Chiranjibi Shah

E-Mail Website
Guest Editor
Northern Gulf Institute, Mississippi State University, Starkville, MS 39759, USA
Interests: machine learning; image processing; pattern recognition; classification; neural networks and artificial intelligence; computer vision; image recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Niamat Ullah Ibne Hossain

E-Mail Website
Guest Editor
Engineering Management Department, Arkansas State University, Jonesboro, AR 72401, USA
Interests: machine learning; data science; hyperspectral imaging; artificial intelligence; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Encompassing approximately three-quarters of the Earth's surface, the ocean plays a crucial role as a source of sustenance, medicine, and commerce. Advancements in ocean observation technologies are transitioning from traditional single-node, static, and short-term modalities to multiple nodes, dynamic, and long-term modalities, aiming to enhance the density of both temporal and spatial samplings.

This issue examines the phenomenon of detecting objects in underwater settings. The most crucial technology for autonomous underwater operations is intelligent computer vision. In underwater environments, it is essential to perform weak illumination and low-quality image enhancement as a preprocessing step for underwater vision. Following image processing, one can suggest employing deep learning-based methods for underwater detection and classification. We invite papers concerning topics including, but not limited to, the following:

  • Underwater visual images detection and classification;
  • Underwater object classification and detection;
  • Deep learning approach;
  • Underwater fish species tracking;
  • Diffusion networks;
  • Fish species detection.

Dr. Chiranjibi Shah
Dr. Niamat Ullah Ibne Hossain
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

  • ocean observation technologies
  • detecting objects in underwater settings
  • underwater vision
  • underwater image processing
  • deep learning
  • underwater detection
  • underwater classification

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Published Papers (3 papers)

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29 pages, 16065 KiB  
Article
Optimization of Adaptive Observation Strategies for Multi-AUVs in Complex Marine Environments Using Deep Reinforcement Learning
by Jingjing Zhang, Weidong Zhou, Xiong Deng, Shuo Yang, Chunwang Yang and Hongliang Yin
J. Mar. Sci. Eng. 2025, 13(5), 865; https://doi.org/10.3390/jmse13050865 (registering DOI) - 26 Apr 2025
Viewed by 102
Abstract
This paper explores the application of Deep Reinforcement Learning (DRL) to optimize adaptive observation strategies for multi-AUV systems in complex marine environments. Traditional algorithms struggle with the strong coupling between environmental information and observation modeling, making it challenging to derive optimal strategies. To [...] Read more.
This paper explores the application of Deep Reinforcement Learning (DRL) to optimize adaptive observation strategies for multi-AUV systems in complex marine environments. Traditional algorithms struggle with the strong coupling between environmental information and observation modeling, making it challenging to derive optimal strategies. To address this, we designed a DRL framework based on the Dueling Double Deep Q-Network (D3QN), enabling AUVs to interact directly with the environment for more efficient 3D dynamic ocean observation. However, traditional D3QN faces slow convergence and weak action–decision correlation in partially observable, dynamic marine settings. To overcome these challenges, we integrate a Gated Recurrent Unit (GRU) into the D3QN, improving state-space prediction and accelerating reward convergence. This enhancement allows AUVs to optimize observations, leverage ocean currents, and navigate obstacles while minimizing energy consumption. Experimental results demonstrate that the proposed approach excels in safety, energy efficiency, and observation effectiveness. Additionally, experiments with three, five, and seven AUVs reveal that while increasing platform numbers enhances predictive accuracy, the benefits diminish with additional units. Full article
(This article belongs to the Special Issue Underwater Observation Technology in Marine Environment)
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22 pages, 13810 KiB  
Article
An Underwater Stereo Matching Method: Exploiting Segment-Based Method Traits without Specific Segment Operations
by Xinlin Xu, Huiping Xu, Lianjiang Ma, Kelin Sun and Jingchuan Yang
J. Mar. Sci. Eng. 2024, 12(9), 1599; https://doi.org/10.3390/jmse12091599 - 10 Sep 2024
Viewed by 1221
Abstract
Stereo matching technology, enabling the acquisition of three-dimensional data, holds profound implications for marine engineering. In underwater images, irregular object surfaces and the absence of texture information make it difficult for stereo matching algorithms that rely on discrete disparity values to accurately capture [...] Read more.
Stereo matching technology, enabling the acquisition of three-dimensional data, holds profound implications for marine engineering. In underwater images, irregular object surfaces and the absence of texture information make it difficult for stereo matching algorithms that rely on discrete disparity values to accurately capture the 3D details of underwater targets. This paper proposes a stereo method based on an energy function of Markov random field (MRF) with 3D labels to fit the inclined plane of underwater objects. Through the integration of a cross-based patch alignment approach with two label optimization stages, the proposed method demonstrates features akin to segment-based stereo matching methods, enabling it to handle images with sparse textures effectively. Through experiments conducted on both simulated UW-Middlebury datasets and real deteriorated underwater images, our method demonstrates superiority compared to classical or state-of-the-art methods by analyzing the acquired disparity maps and observing the three-dimensional reconstruction of the underwater target. Full article
(This article belongs to the Special Issue Underwater Observation Technology in Marine Environment)
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Other

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14 pages, 5331 KiB  
Technical Note
A New Workflow for Instance Segmentation of Fish with YOLO
by Jiushuang Zhang and Yong Wang
J. Mar. Sci. Eng. 2024, 12(6), 1010; https://doi.org/10.3390/jmse12061010 - 18 Jun 2024
Cited by 1 | Viewed by 1754
Abstract
The application of deep-learning technology for marine fishery resource investigation is still in its infancy stage. In this study, we applied YOLOv5 and YOLOv8 methods to identify and segment fish in the seabed. Our results show that both methods could achieve superior performance [...] Read more.
The application of deep-learning technology for marine fishery resource investigation is still in its infancy stage. In this study, we applied YOLOv5 and YOLOv8 methods to identify and segment fish in the seabed. Our results show that both methods could achieve superior performance in the segmentation task of the DeepFish dataset. We also expanded the labeling of specific fish species classification tags on the basis of the original semantic segmentation dataset of DeepFish and completed the multi-class instance segmentation task of fish based on the newly labeled tags. Based on the above two achievements, we propose a general and flexible self-iterative fish identification and segmentation standard workflow that can effectively improve the efficiency of fish surveys. Full article
(This article belongs to the Special Issue Underwater Observation Technology in Marine Environment)
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