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Artificial Intelligence Applications in Underwater Sonar Images
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Artificial Intelligence Applications in Underwater Sonar Images

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

Deadline for manuscript submissions: 10 May 2025 | Viewed by 1145

Special Issue Editors

Dr. Feihu Zhang

E-Mail Website
Guest Editor
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Interests: underwater target detection; information fusion
Dr. Xue Du

E-Mail Website
Guest Editor
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
Interests: underwater sonar processing and navigation; underwater target perception
Dr. Zhixiong Gong

E-Mail Website
Guest Editor
Key Laboratory of Marine Intelligent Equipment and System, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, China
Interests: acoustic characteristics of underwater targets; bubble acoustics; acoustic manipulation of underwater microparticles

Special Issue Information

Dear Colleagues,

The processing of underwater sonar image data has always been a fundamental technology for ocean exploration. In recent years, there has been a growing use of artificial intelligence techniques for analyzing sonar images. Artificial intelligence enhances the accuracy and speed of detecting underwater targets in underwater sonar images and improves the amount of information obtained from the underwater sonar image.

The development of artificial intelligence technology brings breakthroughs and challenges to the technology of underwater sonar image perception. This Special Issue aims to explore the application of AI technology in underwater sonar imagery to effectively address real-world problems and advance the development of AI in the underwater sonar image industry.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Detection of targets in underwater sonar images by deep learning methods.
  • Image segmentation of seabed geomorphological terrain using artificial intelligence techniques.
  • Innovative artificial intelligence models that can address the full-stack problem of underwater sonar images.
  • Acquisition and production of underwater sonar image datasets.
  • Proposing a new paradigm for artificial intelligence techniques to solve the difficult problem of acquiring underwater sonar images.
  • New concepts and advanced sonar detection equipment for underwater sonar images.
  • Underwater simultaneous localization and mapping based on sonar information perception.
  • Underwater acoustic radiation and scattering.

We look forward to receiving your contributions.

Dr. Feihu Zhang
Dr. Xue Du
Dr. Zhixiong Gong
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.

Keywords

  • deep learning on sonar
  • underwater sonar image
  • sonar target detection
  • sonar image segmentation
  • sonar image generator
  • sonar navigation
  • sonar super-resolution imaging
  • underwater sonar equipment
  • underwater sonar signal processing
  • underwater sonar network

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Published Papers (2 papers)

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25 pages, 3467 KiB  
Article
Side-Scan Sonar Small Objects Detection Based on Improved YOLOv11
by Chang Zou, Siquan Yu, Yankai Yu, Haitao Gu and Xinlin Xu
J. Mar. Sci. Eng. 2025, 13(1), 162; https://doi.org/10.3390/jmse13010162 - 18 Jan 2025
Viewed by 481
Abstract
Underwater object detection using side-scan sonar (SSS) remains a significant challenge in marine exploration, especially for small objects. Conventional methods for small object detection face various obstacles, such as difficulties in feature extraction and the considerable impact of noise on detection accuracy. To [...] Read more.
Underwater object detection using side-scan sonar (SSS) remains a significant challenge in marine exploration, especially for small objects. Conventional methods for small object detection face various obstacles, such as difficulties in feature extraction and the considerable impact of noise on detection accuracy. To address these issues, this study proposes an improved YOLOv11 network named YOLOv11-SDC. Specifically, a new Sparse Feature (SF) module is proposed, replacing the Spatial Pyramid Pooling Fast (SPPF) module from the original YOLOv11 architecture to enhance object feature selection. Furthermore, the proposed YOLOv11-SDC integrates a Dilated Reparam Block (DRB) with a C3k2 module to broaden the model’s receptive field. A Content-Guided Attention Fusion (CGAF) module is also incorporated prior to the detection module to assign appropriate weights to various feature maps, thereby emphasizing the relevant object information. Experimental results clearly demonstrate the superiority of YOLOv11-SDC over several iterations of YOLO versions in detection performance. The proposed method was validated through extensive real-world experiments, yielding a precision of 0.934, recall of 0.698, [email protected] of 0.825, and [email protected]:0.95 of 0.598. In conclusion, the improved YOLOv11-SDC offers a promising solution for detecting small objects in SSS images, showing substantial potential for marine applications. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Underwater Sonar Images)
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12 pages, 4954 KiB  
Article
Enhancing Physical Spatial Resolution of Synthetic Aperture Sonar Images Based on Convolutional Neural Network
by Pan Xu, Dongbao Gao, Shui Yu, Guangming Li, Yun Zhao and Guojun Xu
J. Mar. Sci. Eng. 2025, 13(1), 134; https://doi.org/10.3390/jmse13010134 - 14 Jan 2025
Viewed by 439
Abstract
The sonar image has limitations on the physical spatial resolution due to system configuration and underwater environment, which often leads to challenges for underwater targets detection. Here, the deep learning method is applied to enhance the physical spatial resolution of underwater sonar images. [...] Read more.
The sonar image has limitations on the physical spatial resolution due to system configuration and underwater environment, which often leads to challenges for underwater targets detection. Here, the deep learning method is applied to enhance the physical spatial resolution of underwater sonar images. Specifically, the U-shaped end-to-end neural network which contains down-sampling and up-sampling parts is proposed to improve the physical spatial resolution limited by the array aperture. The single target and multiple cases are considered separately. In both cases, the normalized loss on the testing sets declines rapidly, and the predicted high-resolution images own great agreement with the ground truth eventually. Further improvements in resolution are focused on, that is, compressing the predicted high-resolution image to its physical spatial resolution limitation. The results show that the trained end-to-end neural network could map high resolution targets to the impulse responses at the same location and amplitude with an uncertain target number. The proposed convolutional neural network approach could give a practical alternative to improve the physical spatial resolution of underwater sonar images. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Underwater Sonar Images)
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