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Electronics
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Computer Vision for Underwater Object Detection and Classification
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Computer Vision for Underwater Object Detection and Classification

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 14429

Special Issue Editor

Dr. Stanisław Hożyń

E-Mail Website
Guest Editor
Faculty of Mechanical and Electrical Engineering, Polish Naval Academy, 81-127 Gdynia, Poland
Interests: computer vision; machine learning; automation and control systems; underwater vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Underwater object detection and classification—a key element in underwater applications—is crucial in such core areas as industrial security, military operations, and scientific research in marine biology and archaeology, to name but a few. However,  underwater object detection and classification differ from terrestrial tasks due to the effect of water density in limiting light penetration. Additionally, the lack of clarity of the water, turbidity, depth, and surface conditions affect imaging quality. Therefore, underwater object detection and classification are more demanding tasks and require designated technical measures and image processing algorithms.

Sonar systems are often utilized in underwater applications. Since the sonars generate acoustic imaging, which significantly differs from visual images, the image processing techniques need different methods and algorithms. For that reason, there is still space for research in the field of underwater vision.

This Special Issue calls for original research papers that address object detection and classification in the underwater environment. The topics will include the state-of-the-art techniques, solutions, and applications concerning underwater imaging, image processing, and detection and classification algorithms. The most desirable research constitutes artificial intelligence methods, particularly based on neural networks and deep learning. 

Dr. Stanisław Hożyń
Guest Editor

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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • object detection
  • object classification
  • computer vision
  • image processing
  • image analysis
  • image segmentation
  • underwater imaging
  • underwater optics
  • sonar imagining
  • underwater vision

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Published Papers (2 papers)

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Research

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13 pages, 7446 KiB  
Article
6D Pose Estimation for Subsea Intervention in Turbid Waters
by Ahmed Mohammed, Johannes Kvam, Jens T. Thielemann, Karl H. Haugholt and Petter Risholm
Electronics 2021, 10(19), 2369; https://doi.org/10.3390/electronics10192369 - 28 Sep 2021
Cited by 6 | Viewed by 2299
Abstract
Manipulation tasks on subsea instalments require extremely precise detection and localization of objects of interest. This problem is referred to as “pose estimation”. In this work, we present a framework for detecting and predicting 6DoF pose for relevant objects (fish-tail, gauges, and valves) [...] Read more.
Manipulation tasks on subsea instalments require extremely precise detection and localization of objects of interest. This problem is referred to as “pose estimation”. In this work, we present a framework for detecting and predicting 6DoF pose for relevant objects (fish-tail, gauges, and valves) on a subsea panel under varying water turbidity. A deep learning model that takes 3D vision data as an input is developed, providing a more robust 6D pose estimate. Compared to the 2D vision deep learning model, the proposed method reduces rotation and translation prediction error by (−Δ0.39∘) and translation (−Δ6.5 mm), respectively, in high turbid waters. The proposed approach is able to provide object detection as well as 6D pose estimation with an average precision of 91%. The 6D pose estimation results show 2.59∘ and 6.49 cm total average deviation in rotation and translation as compared to the ground truth data on varying unseen turbidity levels. Furthermore, our approach runs at over 16 frames per second and does not require pose refinement steps. Finally, to facilitate the training of such model we also collected and automatically annotated a new underwater 6D pose estimation dataset spanning seven levels of turbidity. Full article
(This article belongs to the Special Issue Computer Vision for Underwater Object Detection and Classification)
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Review

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22 pages, 2178 KiB  
Review
A Review of Underwater Mine Detection and Classification in Sonar Imagery
by Stanisław Hożyń
Electronics 2021, 10(23), 2943; https://doi.org/10.3390/electronics10232943 - 26 Nov 2021
Cited by 37 | Viewed by 10942
Abstract
Underwater mines pose extreme danger for ships and submarines. Therefore, navies around the world use mine countermeasure (MCM) units to protect against them. One of the measures used by MCM units is mine hunting, which requires searching for all the mines in a [...] Read more.
Underwater mines pose extreme danger for ships and submarines. Therefore, navies around the world use mine countermeasure (MCM) units to protect against them. One of the measures used by MCM units is mine hunting, which requires searching for all the mines in a suspicious area. It is generally divided into four stages: detection, classification, identification and disposal. The detection and classification steps are usually performed using a sonar mounted on a ship’s hull or on an underwater vehicle. After retrieving the sonar data, military personnel scan the seabed images to detect targets and classify them as mine-like objects (MLOs) or benign objects. To reduce the technical operator’s workload and decrease post-mission analysis time, computer-aided detection (CAD), computer-aided classification (CAC) and automated target recognition (ATR) algorithms have been introduced. This paper reviews mine detection and classification techniques used in the aforementioned systems. The author considered current and previous generation methods starting with classical image processing, and then machine learning followed by deep learning. This review can facilitate future research to introduce improved mine detection and classification algorithms. Full article
(This article belongs to the Special Issue Computer Vision for Underwater Object Detection and Classification)
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