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Special Issue "Underwater Vision Sensing System"

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

Deadline for manuscript submissions: 25 April 2024 | Viewed by 1584

Special Issue Editors

College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
Interests: underwater vision; artificial intelligence
Dr. Sai-Kit Yeung
E-Mail Website
Guest Editor
Division of Integrative Systems and Design (ISD) and Department of Computer Science and Engineering (CSE), Hong Kong University of Science and Technology, Hong Kong SAR 999077, China
Interests: underwater vision; marine vision; underwater scene understanding
Faculty of Engineering Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Fukuoka 804-0015, Japan
Interests: robotics; oceanic optics; computer vision; artificial Intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image and video sensing are essential tools for exploring and understanding the hidden world beneath the waves, yet the complex aquatic environment has prevented them from being widely deployed. Recent advances in underwater vision sensing technology have enabled researchers to develop efficient and cost-effective systems for underwater imaging. Underwater vision sensing systems can be used for a variety of applications, such as underwater surveillance and tracking, monitoring of marine habitats and species, navigation and mapping, and exploration.

The goal of this Special Issue is to introduce recent advances in underwater vision sensing systems, which involve autonomous underwater vehicles, sonar imaging, optical imaging, 3D reconstruction, automatic driving devices, sonar system optimization, the Internet of Things, security facilities, navigation systems, computer vision devices, acoustic materials, acoustic technologies, and so on. In this Special Issue, we expect to publish papers with theoretical and practical innovations in underwater vision sensing systems involving underwater optical and sonar imaging, underwater sensor design and development, image and signal processing for underwater vision sensing, image and signal analysis for object recognition and tracking, machine learning and artificial intelligence for underwater vision sensing, underwater navigation and localization, underwater communication and networking, applications of underwater vision sensing in autonomous underwater vehicles, and any other possible applications.

Topics of interest include, but are not limited to:

Underwater imaging;

Acoustic technology;

The implementation of deep learning in underwater vision systems;

Underwater 3D reconstruction;

Remotely operated vehicle;

Autonomous underwater vehicles;

Underwater stereo vision;

Underwater monitoring;

Image and signal processing in underwater vision sensing systems;

Underwater networks;

Underwater communication;

Underwater sensors;

Underwater materials;

Underwater devices;

Underwater navigation;

Underwater sensing and detection;

Underwater microscopy.

Dr. Zhibin Yu
Dr. Sai-Kit Yeung
Dr. Huimin Lu
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. Sensors is an international peer-reviewed open access semimonthly 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.

Published Papers (3 papers)

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Research

Article
A Novel Cone Model Filtering Method for Outlier Rejection of Multibeam Bathymetric Point Cloud: Principles and Applications
Sensors 2023, 23(17), 7483; https://doi.org/10.3390/s23177483 - 28 Aug 2023
Viewed by 414
Abstract
The utilization of multibeam sonar systems has significantly facilitated the acquisition of underwater bathymetric data. However, efficiently processing vast amounts of multibeam point cloud data remains a challenge, particularly in terms of rejecting massive outliers. This paper proposes a novel solution by implementing [...] Read more.
The utilization of multibeam sonar systems has significantly facilitated the acquisition of underwater bathymetric data. However, efficiently processing vast amounts of multibeam point cloud data remains a challenge, particularly in terms of rejecting massive outliers. This paper proposes a novel solution by implementing a cone model filtering method for multibeam bathymetric point cloud data filtering. Initially, statistical analysis is employed to remove large-scale outliers from the raw point cloud data in order to enhance its resistance to variance for subsequent processing. Subsequently, virtual grids and voxel down-sampling are introduced to determine the angles and vertices of the model within each grid. Finally, the point cloud data was inverted, and the custom parameters were redefined to facilitate bi-directional data filtering. Experimental results demonstrate that compared to the commonly used filtering method the proposed method in this paper effectively removes outliers while minimizing excessive filtering, with minimal differences in standard deviations from human-computer interactive filtering. Furthermore, it yields a 3.57% improvement in accuracy compared to the Combined Uncertainty and Bathymetry Estimator method. These findings suggest that the newly proposed method is comparatively more effective and stable, exhibiting great potential for mitigating excessive filtering in areas with complex terrain. Full article
(This article belongs to the Special Issue Underwater Vision Sensing System)
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Article
Densely Connected Networks with Multiple Features for Classifying Sound Signals with Reverberation
Sensors 2023, 23(16), 7225; https://doi.org/10.3390/s23167225 - 17 Aug 2023
Viewed by 326
Abstract
In indoor environments, reverberation can distort the signalseceived by active noise cancelation devices, posing a challenge to sound classification. Therefore, we combined three speech spectral features based on different frequency scales into a densely connected network (DenseNet) to accomplish sound classification with reverberation [...] Read more.
In indoor environments, reverberation can distort the signalseceived by active noise cancelation devices, posing a challenge to sound classification. Therefore, we combined three speech spectral features based on different frequency scales into a densely connected network (DenseNet) to accomplish sound classification with reverberation effects. We adopted the DenseNet structure to make the model lightweight A dataset was created based on experimental and simulation methods, andhe classification goal was to distinguish between music signals, song signals, and speech signals. Using this framework, effectivexperiments were conducted. It was shown that the classification accuracy of the approach based on DenseNet and fused features reached 95.90%, betterhan the results based on other convolutional neural networks (CNNs). The size of the optimized DenseNet model is only 3.09 MB, which is only 7.76% of the size before optimization. We migrated the model to the Android platform. The modified model can discriminate sound clips faster on Android thanhe network before the modification. This shows that the approach based on DenseNet and fused features can dealith sound classification tasks in different indoor scenes, and the lightweight model can be deployed on embedded devices. Full article
(This article belongs to the Special Issue Underwater Vision Sensing System)
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Article
Estimation of Target Motion Parameters from the Tonal Signals with a Single Hydrophone
Sensors 2023, 23(15), 6881; https://doi.org/10.3390/s23156881 - 03 Aug 2023
Viewed by 296
Abstract
In the shallow-water waveguide environment, the tonal signals radiated by moving targets carry modal interference and Doppler shift information. The modal interference can be used to obtain the time of the closest point of approach (tCPA) and the [...] Read more.
In the shallow-water waveguide environment, the tonal signals radiated by moving targets carry modal interference and Doppler shift information. The modal interference can be used to obtain the time of the closest point of approach (tCPA) and the ratio of the range at the closest point of approach to the velocity of the source (rCPA/v). However, parameters rCPA and v cannot be solved separately. When tCPA is known, the rCPA and the v of the target can be obtained theoretically by using the Doppler information. However, when the Doppler frequency shift is small or at a low signal-to-noise ratio, there will be a strong parametric coupling between rCPA and v. In order to solve the above parameter coupling problem, a target motion parameter estimation method from tonal signals with a single hydrophone is proposed in this paper. The method uses the Doppler and modal interference information carried by the tonal signals to obtain two different parametric coupling curves. Then, the parametric coupling curves can be used to estimate the two motion parameters. Simulation experiments verified the rationality of this method. The proposed method was applied to the SWellEx-96 and speedboat experiments, and the estimation errors of the motion parameters were within 10%, which shows the method is effective in its practical applications. Full article
(This article belongs to the Special Issue Underwater Vision Sensing System)
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