Development of a Second-Generation Underwater Acoustic Ambient Noise Imaging Camera

Pallayil, Venugopalan; Kuselan, Subash; Raichur, Amogh; Ignatius, Manu; Potter, John R.

doi:10.1109/joe.2014.2386783

Cited by 10 publications

(1 citation statement)

References 16 publications

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“…In comparison with the transmission distance limitation of radio waves in the water, which results from serious attenuation, sound wave is proved to be an ideal transmission carrier in underwater environments. Forward-looking sonars have been widely applied in the field of underwater engineering, such as object detection, [1][2][3] target tracking, [4][5][6][7] AUV navigation, 8,9) and obstacle avoidance, 10) because of its relatively high frame rate and resolution. Sonar image segmentation on the gray level contributes to underwater linear object detection as an irreplaceable basis of unsupervised visual understanding.…”

Section: Introductionmentioning

confidence: 99%

A prior-knowledge-based threshold segmentation method of forward-looking sonar images for underwater linear object detection

Liu

Bian

Yagi

et al. 2016

Jpn. J. Appl. Phys.

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Raw sonar images may not be used for underwater detection or recognition directly because disturbances such as the grating-lobe and multi-path disturbance affect the gray-level distribution of sonar images and cause phantom echoes. To search for a more robust segmentation method with a reasonable computational cost, a prior-knowledge-based threshold segmentation method of underwater linear object detection is discussed. The possibility of guiding the segmentation threshold evolution of forward-looking sonar images using prior knowledge is verified by experiment. During the threshold evolution, the collinear relation of two lines that correspond to double peaks in the voting space of the edged image is used as the criterion of termination. The interaction is reflected in the sense that the Hough transform contributes to the basis of the collinear relation of lines, while the binary image generated from the current threshold provides the resource of the Hough transform. The experimental results show that the proposed method could maintain a good tradeoff between the segmentation quality and the computational time in comparison with conventional segmentation methods. The proposed method redounds to a further process for unsupervised underwater visual understanding.

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Section: Introductionmentioning

confidence: 99%

A prior-knowledge-based threshold segmentation method of forward-looking sonar images for underwater linear object detection

Liu

Bian

Yagi

et al. 2016

Jpn. J. Appl. Phys.

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Acousto-optical metasurfaces for high-resolution acoustic imaging systems

Cheong

Popa

2021

Phys. Rev. B

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Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey

Jahanbakht

Wang

Hanzo

et al. 2021

IEEE Commun. Surv. Tutorials

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The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed. Accordingly, the reader will become familiar with the pivotal issues of IoUT and BMD processing, whilst gaining an insight into the state-of-theart applications, tools, and techniques. Finally, we analyze of the architectural challenges of the IoUT, followed by proposing a range of promising direction for research and innovation in the broad areas of IoUT and BMD. Our hope is to inspire researchers, engineers, data scientists, and governmental bodies to further progress the field, to develop new tools and techniques, as well as to make informed decisions and set regulations related to the maritime and underwater environments around the world.

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Development of a Second-Generation Underwater Acoustic Ambient Noise Imaging Camera

Cited by 10 publications

References 16 publications

A prior-knowledge-based threshold segmentation method of forward-looking sonar images for underwater linear object detection

A prior-knowledge-based threshold segmentation method of forward-looking sonar images for underwater linear object detection

Acousto-optical metasurfaces for high-resolution acoustic imaging systems

Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey

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