A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition
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 a...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-11-01
|
Series: | Electronics |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-9292/9/11/1972 |
id |
doaj-6f641c7a14694c34949dc264e01de954 |
---|---|
record_format |
Article |
spelling |
doaj-6f641c7a14694c34949dc264e01de9542020-11-25T04:11:29ZengMDPI AGElectronics2079-92922020-11-0191972197210.3390/electronics9111972A Review on Deep Learning-Based Approaches for Automatic Sonar Target RecognitionDhiraj Neupane0Jongwon Seok1Department of Information and Communication Engineering, Changwon National University, Changwon-si, Gyeongsangnam-do 51140, KoreaDepartment of Information and Communication Engineering, Changwon National University, Changwon-si, Gyeongsangnam-do 51140, KoreaUnderwater 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.https://www.mdpi.com/2079-9292/9/11/1972sonar systemdeep learningactive sonarpassive sonarside-scan sonarautomatic target recognition |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dhiraj Neupane Jongwon Seok |
spellingShingle |
Dhiraj Neupane Jongwon Seok A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition Electronics sonar system deep learning active sonar passive sonar side-scan sonar automatic target recognition |
author_facet |
Dhiraj Neupane Jongwon Seok |
author_sort |
Dhiraj Neupane |
title |
A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition |
title_short |
A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition |
title_full |
A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition |
title_fullStr |
A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition |
title_full_unstemmed |
A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition |
title_sort |
review on deep learning-based approaches for automatic sonar target recognition |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2020-11-01 |
description |
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. |
topic |
sonar system deep learning active sonar passive sonar side-scan sonar automatic target recognition |
url |
https://www.mdpi.com/2079-9292/9/11/1972 |
work_keys_str_mv |
AT dhirajneupane areviewondeeplearningbasedapproachesforautomaticsonartargetrecognition AT jongwonseok areviewondeeplearningbasedapproachesforautomaticsonartargetrecognition AT dhirajneupane reviewondeeplearningbasedapproachesforautomaticsonartargetrecognition AT jongwonseok reviewondeeplearningbasedapproachesforautomaticsonartargetrecognition |
_version_ |
1724417527927275520 |