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Underwater Target Detection and Recognition
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Underwater Target Detection and Recognition

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Physical Oceanography".

Deadline for manuscript submissions: closed (15 December 2024) | Viewed by 9520

Special Issue Editors

Dr. Youngmin Choo

E-Mail Website
Guest Editor
Department of Ocean Systems Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: reverberation and scattering by rough boundaries; underwater acoustic propagation including time varying acoustic channel by travelling surface wave; sonar signal processing for target detection and localization using compressive sensing and machine/deep learning
Prof. Dr. Woojae Seong

E-Mail Website
Guest Editor
Department of Naval Architecture and Ocean Engineering and Research Institute of Marine System Engineering, Seoul National University, Seoul 08826, Republic of Korea
Interests: directiion-of-arrival; acoustics, underwater sound, ocean engineering
Dr. Haesang Yang

E-Mail Website
Guest Editor
Department of Naval Architecture and Ocean Engineering, Seoul National University, Seoul, Republic of Korea
Interests: object detection; deep learning; acoustics; acoustic propagation; acoustic wave velocity

Special Issue Information

Dear Colleagues,

One important purpose of operating sonar systems is to either passively or actively detect signals from targets of interest. Recently, passive target detection has become more difficult owing to a lower level of underwater radiated noise from targets as well as growing ocean ambient noise. Alternatively, an active sonar system can be applied to detect bounce signals returning from the target with a higher signal-to-noise ratio (SNR) by exploiting the information of known transmitted signals at the cost of false alarms by clutters. Many studies have been conducted to advance target detection and recognition in the underwater acoustics community.

This Special Issue seeks the submission of studies on advanced underwater signal processing for target detection and recognition, which can overcome the aforementioned problems (or other problems) in passive or active sonar systems.

Dr. Youngmin Choo
Prof. Dr. Woojae Seong
Dr. Haesang Yang
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.

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Published Papers (7 papers)

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Research

32 pages, 4011 KiB  
Article
Enhancing Underwater Acoustic Target Recognition Through Advanced Feature Fusion and Deep Learning
by Yanghong Zhao, Guohao Xie, Haoyu Chen, Mingsong Chen and Li Huang
J. Mar. Sci. Eng. 2025, 13(2), 278; https://doi.org/10.3390/jmse13020278 - 31 Jan 2025
Viewed by 382
Abstract
Underwater Acoustic Target Recognition (UATR) is critical to maritime traffic management and ocean monitoring. However, underwater acoustic analysis is fraught with difficulties. The underwater environment is highly complex, with ambient noise, variable water conditions (such as temperature and salinity), and multi-path propagation of [...] Read more.
Underwater Acoustic Target Recognition (UATR) is critical to maritime traffic management and ocean monitoring. However, underwater acoustic analysis is fraught with difficulties. The underwater environment is highly complex, with ambient noise, variable water conditions (such as temperature and salinity), and multi-path propagation of acoustic signals. These factors make it challenging to accurately acquire and analyze target features. Traditional UATR methods struggle with feature fusion representations and model generalization. This study introduces a novel high-dimensional feature fusion method, CM3F, grounded in signal analysis and brain-like features, and integrates it with the Boundary-Aware Hybrid Transformer Network (BAHTNet), a deep-learning architecture tailored for UATR. BAHTNet comprises CBCARM and XCAT modules, leveraging a Kan network for classification and a large-margin aware focal (LMF) loss function for predictive losses. Experimental results on real-world datasets demonstrate the model’s robust generalization capabilities, achieving 99.8% accuracy on the ShipsEar dataset and 94.57% accuracy on the Deepship dataset. These findings underscore the potential of BAHTNet to significantly improve UATR performance. Full article
(This article belongs to the Special Issue Underwater Target Detection and Recognition)
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13 pages, 3150 KiB  
Article
Underwater Target Detection with High Accuracy and Speed Based on YOLOv10
by Zhengliang Hu, Le Cheng, Shui Yu, Pan Xu, Peng Zhang, Rui Tian and Jingqi Han
J. Mar. Sci. Eng. 2025, 13(1), 135; https://doi.org/10.3390/jmse13010135 - 14 Jan 2025
Viewed by 460
Abstract
Underwater target detection exhibits extensive applications in marine target exploration and marine environmental monitoring. However, conventional images of underwater targets present challenges including blurred contour information, complex environmental conditions, and pronounced scattering effects. In this work, an underwater target detection method based on [...] Read more.
Underwater target detection exhibits extensive applications in marine target exploration and marine environmental monitoring. However, conventional images of underwater targets present challenges including blurred contour information, complex environmental conditions, and pronounced scattering effects. In this work, an underwater target detection method based on YOLOv10 is designed, and the detection performance is compared with the YOLOv5 model. Experimental results demonstrate that the YOLOv10 model has a mAP50 of 85.6% on the URPC 2020 dataset, improving the mAP50 by 1.2% than that of YOLOv5. This model exhibits high detection accuracy and high proceeding speed, which provides a promising support for precise and fast underwater target detection. Full article
(This article belongs to the Special Issue Underwater Target Detection and Recognition)
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19 pages, 8828 KiB  
Article
Bearing-Only Multi-Target Localization Incorporating Waveguide Characteristics for Low Detection Rate Scenarios in Shallow Water
by Xiaohan Mei, Bo Zhang, Duo Zhai and Zhaohui Peng
J. Mar. Sci. Eng. 2024, 12(12), 2300; https://doi.org/10.3390/jmse12122300 - 13 Dec 2024
Viewed by 637
Abstract
Bearing-only multi-target localization (BOMTL) determines the positions of multiple targets by intersecting bearing lines from multiple spatial locations. However, non-ideal measurements can result in a large number of ghost targets. A β-S-dimensional assignment (β-SDA) method incorporating waveguide characteristics is proposed [...] Read more.
Bearing-only multi-target localization (BOMTL) determines the positions of multiple targets by intersecting bearing lines from multiple spatial locations. However, non-ideal measurements can result in a large number of ghost targets. A β-S-dimensional assignment (β-SDA) method incorporating waveguide characteristics is proposed to address the BOMTL problem in shallow water with low detection rates. The estimated distance for the warping transformation is derived from the intersection points of the bearing lines, then the autocorrelation function of the broadband beamforming output is transformed using a warping operator to obtain the corresponding characteristic spectrum. The peaks in the characteristic spectrum correspond to the cross-correlation terms of the normal modes, with the frequencies of these peaks related to the ratio of the actual distance to the estimated distance of the sound source. The global target localization results are obtained using the proposed method, which incorporates confidence coefficients derived from the characteristic spectrum and geometric intersection information from the bearing lines. Simulation and sea trial data demonstrate that the β-SDA method effectively overcomes the limitation of pure bearing-only localization in low detection rate scenarios under a given signal-to-noise ratio (SNR), and can localize target positions without requiring precise prior environmental parameters. Full article
(This article belongs to the Special Issue Underwater Target Detection and Recognition)
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11 pages, 1737 KiB  
Article
Snapping Shrimp Noise Detection Based on Statistical Model
by Suhyeon Park, Jongwon Seok and Jungpyo Hong
J. Mar. Sci. Eng. 2024, 12(1), 42; https://doi.org/10.3390/jmse12010042 - 23 Dec 2023
Cited by 1 | Viewed by 1530
Abstract
Snapping Shrimps (SSs) live in a warm ocean except the North and South Poles, and they are characterized by generating strong shock waves underwater using large claws. Shock waves generated by these SSs are used for marine noise research as a signal and [...] Read more.
Snapping Shrimps (SSs) live in a warm ocean except the North and South Poles, and they are characterized by generating strong shock waves underwater using large claws. Shock waves generated by these SSs are used for marine noise research as a signal and as a noise source, because they cause a decrease in the Signal-to-Noise Ratio (SNR), acting as one of the disruptors in fields such as sonar for target detection and underwater communication. A state-of-the-art technique to detect Snapping Shrimp Noise (SSN) is Linear Prediction (LP) analysis. Using the feature where SSN occurs for a very short time, the SSN interval was detected based on the phenomenon where the residuals appear large in the SSN interval when the LP analysis is used. In this paper, we propose an SSN interval detection technique using the Likelihood Ratio (LR) as a follow-up study to the LP-analysis-based method for further performance improvements. The proposed method was used to analyze the statistical distribution characteristics of the LP residual of SSNs compared to Gaussian, Laplace, and Gamma distributions through the Goodness-Of-Fit test. Based on this, the statistical-model-based LRs of the three distributions were computed to detect the SSN interval. Comparing the proposed method with the state-of-the-art method, the proposed method achieved 0.0620, 0.0675, and 0.0662 improvements in Gaussian, Laplace, and Gamma distributions in the Receiver Operating Characteristic curve and Area Under Curve, respectively. The study results confirmed that the proposed method can operate effectively in the marine acoustic environment. This can help find accurate intervals for the automatic labeling of or reduction in SSN. Full article
(This article belongs to the Special Issue Underwater Target Detection and Recognition)
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21 pages, 3609 KiB  
Article
A Contrastive-Learning-Based Method for the Few-Shot Identification of Ship-Radiated Noises
by Leixin Nie, Chao Li, Haibin Wang, Jun Wang, Yonglin Zhang, Fan Yin, Franck Marzani and Alexis Bozorg Grayeli
J. Mar. Sci. Eng. 2023, 11(4), 782; https://doi.org/10.3390/jmse11040782 - 4 Apr 2023
Cited by 5 | Viewed by 1812
Abstract
For identifying each vessel from ship-radiated noises with only a very limited number of data samples available, an approach based on the contrastive learning was proposed. The input was sample pairs in the training, and the parameters of the models were optimized by [...] Read more.
For identifying each vessel from ship-radiated noises with only a very limited number of data samples available, an approach based on the contrastive learning was proposed. The input was sample pairs in the training, and the parameters of the models were optimized by maximizing the similarity of sample pairs from the same vessel and minimizing that from different vessels. In practical inference, the method calculated the distance between the features of testing samples and those of registration templates and assigned the testing sample into the closest templates for it to achieve the parameter-free classification. Experimental results on different sea-trial data demonstrated the advantages of the proposed method. On the five-ship identification task based on the open-source data, the proposed method achieved an accuracy of 0.68 when only five samples per vessel were available, that was significantly higher than conventional solutions with accuracies of 0.26 and 0.48. Furthermore, the convergence of the method and the behavior of its performance with increasing data samples available for the training were discussed empirically. Full article
(This article belongs to the Special Issue Underwater Target Detection and Recognition)
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13 pages, 3913 KiB  
Article
Ray-Based Analysis of Subcritical Scattering from Buried Target
by Yeon-Seong Choo, Giyung Choi, Keunhwa Lee, Sung-Hoon Byun and Youngmin Choo
J. Mar. Sci. Eng. 2023, 11(2), 307; https://doi.org/10.3390/jmse11020307 - 1 Feb 2023
Cited by 1 | Viewed by 1548
Abstract
A ray approach is used to simulate subcritical scattering from a buried target at low-to-high frequencies (100 Hz–15 kHz). A penetrating wave at a subcritical angle decays along the depth at the bottom (i.e., evanescent wave) and propagates horizontally at a subcritical angle-dependent [...] Read more.
A ray approach is used to simulate subcritical scattering from a buried target at low-to-high frequencies (100 Hz–15 kHz). A penetrating wave at a subcritical angle decays along the depth at the bottom (i.e., evanescent wave) and propagates horizontally at a subcritical angle-dependent speed lower than the sound speed of the bottom. The corresponding target strength (TS) is distinguished from that of a standard plane wave. Its pattern is asymmetric by the evanescent wave including for symmetric targets and is more complicated owing to the higher wavenumber induced by the lower speed of the evanescent wave. A scattered signal is simulated by considering the features of the penetrating wave with the TS and then verified using the finite element method. In the ray approach, once the TS is computed, a scattered field is efficiently derived with low computational complexity. Strong peaks are observed in the scattered signal via mid-frequency enhancement; however, their amplitudes are less than those yielded by the free-field target owing to the more diminished penetrating waves at higher frequencies. The peaks indicate the possibility of detecting the buried target using a receiver near the target (bistatic sonar) with a broadband source signal that includes low-to-mid frequencies. Full article
(This article belongs to the Special Issue Underwater Target Detection and Recognition)
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15 pages, 20183 KiB  
Article
Least Mean p-Power-Based Sparsity-Driven Adaptive Line Enhancer for Passive Sonars Amid Under-Ice Noise
by Yujiao Lv, Cheng Chi, Haining Huang and Shenglong Jin
J. Mar. Sci. Eng. 2023, 11(2), 269; https://doi.org/10.3390/jmse11020269 - 24 Jan 2023
Cited by 5 | Viewed by 1716
Abstract
In order to detect weak underwater tonals, adaptive line enhancers (ALEs) have been widely applied in passive sonars. Unfortunately, conventional ALEs cannot perform well amid impulse noise generated by ice cracking, snapping shrimp or other factors. This kind of noise has a different [...] Read more.
In order to detect weak underwater tonals, adaptive line enhancers (ALEs) have been widely applied in passive sonars. Unfortunately, conventional ALEs cannot perform well amid impulse noise generated by ice cracking, snapping shrimp or other factors. This kind of noise has a different noise model compared to Gaussian noise and leads to noise model mismatch problems in conventional ALEs. To mitigate the performance degradation of conventional ALEs in under-ice impulse noise, in this study, a modified ALE is proposed for passive sonars. The proposed ALE is based on the least mean p-power (LMP) error criterion and the prior information of the frequency domain sparsity to improve the enhancement performance under impulse noise. The signal-to-noise ratio (SNR) gain is chosen as the metric for evaluating the proposed ALE. The simulation results show that the output SNR gain of the proposed ALE was, respectively, 9.3 and 2.6 dB higher than that of the sparsity-based ALE (SALE) and the least mean p-power ALE (PALE) when the input GSNR was −12 dB. The results of processing the under-ice noise data also demonstrate that the proposed ALE is distinguished among the four ALEs. Full article
(This article belongs to the Special Issue Underwater Target Detection and Recognition)
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