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Cited by 192Underwater Acoustics ResearchUnderwater Vehicles and Communication SystemsMusic and Audio Processing

A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

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Neupane, Dhiraj, Seok, Jongwon·2020·Electronics
DOI 10.3390/electronics9111972
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Abstract

Underwater acoustics has been implemented mostly in the field of sound navigation and ranging (SONAR) procedures for submarine communication, the examination of maritime assets and environment surveying, target and object recognition, and measurement and study of acoustic sources in the underwater atmosphere. With the rapid development in science and technology, the advancement in sonar systems has increased, resulting in a decrement in underwater casualties. The sonar signal processing and automatic target recognition using sonar signals or imagery is itself a challenging process. Meanwhile, highly advanced data-driven machine-learning and deep learning-based methods are being implemented for acquiring several types of information from underwater sound data. This paper reviews the recent sonar automatic target recognition, tracking, or detection works using deep learning algorithms. A thorough study of the available works is done, and the operating procedure, results, and other necessary details regarding the data acquisition process, the dataset used, and the information regarding hyper-parameters is presented in this article. This paper will be of great assistance for upcoming scholars to start their work on sonar automatic target recognition.

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  • There is a notable scarcity of publicly available curated and annotated datasets in underwater acoustic data classification.

    “This paper also summarises some of the most important datasets cited in the literature, pointing out the notable scarcity of publicly available curated and annotated datasets (an issue that is often reported in research papers in this field [18]).”

    From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [18]

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Authors
Neupane, D., & Seok, J.
Affiliations
Changwon National University (KR)
Corresponding author
Seok, Jongwon
ORCID
Venue
Electronics
Publisher
MDPI AG
Published
2020-11-22
Volume
9
Issue
11
Pages
1972
ISSN
2079-9292 (ISSN-L)
DOI
10.3390/electronics9111972
OpenAlex
W3109637960
Cited by
192
References
99
Citations by year
2026: 25 · 2025: 41 · 2024: 45 · 2023: 41 · 2022: 28 · 2021: 12
FWCI
9.72
Type
journal article
Language
en
Open access
Licence
Locations
  • Electronics (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Ministry of Trade, Industry and Energy
Topics
  • Physical Sciences › Earth and Planetary Sciences › Oceanography › Underwater Acoustics Research
  • Physical Sciences › Engineering › Ocean Engineering › Underwater Vehicles and Communication Systems
  • Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
Keywords
sonar target recognition, automatic target recognition, underwater acoustics, sonar signal processing, sonar images, target detection, target tracking, deep learning

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