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Special Issue "Underwater Vision Sensing System"

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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: 25 April 2024 | Viewed by 1584

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Special Issue Editors

Dr. Zhibin Yu

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Guest Editor

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

Dr. Huimin Lu

E-Mail Website
Guest Editor

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
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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

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Published Papers (3 papers)

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Research

Open AccessArticle

A Novel Cone Model Filtering Method for Outlier Rejection of Multibeam Bathymetric Point Cloud: Principles and Applications

and

Sensors 2023, 23(17), 7483; https://doi.org/10.3390/s23177483 - 28 Aug 2023

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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 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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Open AccessArticle

Densely Connected Networks with Multiple Features for Classifying Sound Signals with Reverberation

and

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 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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Open AccessArticle

Estimation of Target Motion Parameters from the Tonal Signals with a Single Hydrophone

and

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 (

t_{C P A}

) and the [...] Read more.

t_{C P A}

) and the ratio of the range at the closest point of approach to the velocity of the source (

r_{C P A} / v

). However, parameters

r_{C P A}

and v cannot be solved separately. When

t_{C P A}

is known, the

r_{C P A}

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

r_{C P A}

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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