Convergence and divergence between two multibeam sonars (SIMRAD SM20 and RESON SeaBat 6012) used to extract the spatial, morphologic and energy parameters of fish schools

Perrot, Yannick; Guillard, Jean; Josse, Erwan

doi:10.1016/j.fishres.2010.09.007

Cited by 8 publications

(3 citation statements)

References 43 publications

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“…The MBES has been used for a variety of applications, 74,75 including military, aquaculture, oceanographic 76 and seafloor geological and benthic habitat mapping 77,78 . However, this MBES technology is rarely used in recirculating aquaculture systems, and it is only used in aquaculture cages or aquaculture ponds 79 .…”

Section: Acoustic Technologiesmentioning

confidence: 99%

Recent advances in acoustic technology for aquaculture: A review

Wang

et al. 2023

Reviews in Aquaculture

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Acoustic technology has great application prospects in aquaculture. In particular, two indispensable, critical technologies for the future aquaculture industry are multi‐sensor acquisition that can achieve multi‐scale information fusion, collection and establishment of a global acoustic fish database and highly developed deep learning intelligent algorithms that can establish a correlation mechanism between fish acoustic and behaviour characteristics. Acoustic technology offers remarkable advantages in large and turbid water bodies for studying spatial and temporal distribution patterns of aquatic organism populations, developing on‐demand feeding systems and estimating biomass. This article reviews the development of acoustic technology and its application in aquaculture over the last 30 years. It further analyses, in detail, the advantages and disadvantages of acoustic technology in evaluating aquatic organism biomass and morphological and physical indicators, aquatic organism behaviour and welfare improvement. Challenges of acoustic technology in acquiring dynamic target data accurately, building a global acoustic fish database and establishing connections between fish behaviour and acoustic characteristics are also discussed. In brief, this article aims to help researchers and practitioners better understand the current state‐of‐the‐art acoustic technologies, which can provide strong support for smart aquaculture applications.

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Section: Acoustic Technologiesmentioning

confidence: 99%

Recent advances in acoustic technology for aquaculture: A review

Wang

et al. 2023

Reviews in Aquaculture

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“…Indeed, the wide area insonified with a single swath (typically covering about 120°) differentiates MBESs from SBES whose main beam widens less than 20° and therefore requires far more survey time to achieve equivalent coverage. Novel and compelling applications of WCI from MBES range from qualitative descriptions of fish school behavior and morphological characteristics [ 11 , 12 , 13 , 14 ], oil and gas leakage detection [ 15 , 16 , 17 , 18 ], kelp ecosystem mapping [ 19 , 20 ] and aquaculture monitoring [ 21 ], to assessing the mean abundance of marine macro-litter [ 22 , 23 ] and providing other valuable insights to marine systems [ 24 , 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Semi-Automated Data Processing and Semi-Supervised Machine Learning for the Detection and Classification of Water-Column Fish Schools and Gas Seeps with a Multibeam Echosounder

Minelli

Tassetti

Hutton³

et al. 2021

Sensors

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Multibeam echosounders are widely used for 3D bathymetric mapping, and increasingly for water column studies. However, they rapidly collect huge volumes of data, which poses a challenge for water column data processing that is often still manual and time-consuming, or affected by low efficiency and high false detection rates if automated. This research describes a comprehensive and reproducible workflow that improves efficiency and reliability of target detection and classification, by calculating metrics for target cross-sections using a commercial software before feeding into a feature-based semi-supervised machine learning framework. The method is tested with data collected from an uncalibrated multibeam echosounder around an offshore gas platform in the Adriatic Sea. It resulted in more-efficient target detection, and, although uncertainties regarding user labelled training data need to be underlined, an accuracy of 98% in target classification was reached by using a final pre-trained stacking ensemble model.

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“…One of the important tasks is to discriminate the potential object from the background in acoustic image. The capacity to automatically detect multiple objects is essential for the applications such as marine geology, commercial fishing, monitoring of harbour or coastal areas, examination of suspended sediment [2–6]. Nevertheless, objects' appearances can change significantly from frame to frame [7, 8], due to objects motion, inhomogeneous insonification, low signal‐to‐noise ratio (SNR) and poor resolution.…”

Section: Introductionmentioning

confidence: 99%