Imaging sonar-based fish detection in shallow waters

Wolff, Lars Michael; Badri-Hoeher, Sabah

doi:10.1109/oceans.2014.7003213

Cited by 17 publications

(21 citation statements)

References 7 publications

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“…The forward looking sonar is a BlueView M900-130 acoustical camera [51], mounted 5°downward from the horizontal on the front of the OFOS, giving a view angle of 130°. The primary function of the device is to aid in hazard avoidance via real-time modification of the ships heading, particularly on modern research vessels equipped with dynamic positioning systems.…”

Section: F Sonar Systemsmentioning

confidence: 99%

Ocean Floor Observation and Bathymetry System (OFOBS): A New Towed Camera/Sonar System for Deep-Sea Habitat Surveys

Purser

Marcon

Dreutter

et al. 2019

IEEE J. Oceanic Eng.

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Towed camera systems are commonly used to collect photo and video images of the deep seafloor for a wide variety of purposes, from pure exploratory research to the development of management plans. Ongoing technological developments are increasing the quantity and quality of data collected from the deep seafloor. Despite these improvements, the area of seafloor, which towed systems can survey, optically remains limited by the rapid attenuation of visible wavelengths within water. We present an overview of a new towed camera platform integrating additional acoustical devices: the ocean floor observation and bathymetry system (OFOBS). The towed system maintains continuous direct communication via fiber optic cable with a support vessel, operational at depths up to 6000 m. In addition to collecting seafloor photo and video data, OFOBS gathers sidescan data over a 100-m swath width. OFOBS functionality is further augmented by a forward looking sonar, used to aid in hazard avoidance and real-time course correction. Data collected during the first field deployments of OFOBS, at a range of seamounts on the Langseth Ridge/Gakkel Ridge intersection (86°N , 61°E) in the high Arctic in September 2016, are presented to demonstrate the functionality of the system. Collected from a location with near continuous ice cover, this explanatory data set highlights the advantages of the system for deep-sea survey work in environments currently difficult to access for the majority of subsurface research platforms.

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Section: F Sonar Systemsmentioning

confidence: 99%

Ocean Floor Observation and Bathymetry System (OFOBS): A New Towed Camera/Sonar System for Deep-Sea Habitat Surveys

Purser

Marcon

Dreutter

et al. 2019

IEEE J. Oceanic Eng.

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“…This approach simulates stations with underwater sonar sensors that can detect fish [ 40 ]. These stations also have sonar sensors that can also detect other stations with sonar sensors.…”

Section: Abs-fishcountmentioning

confidence: 99%

ABS-FishCount: An Agent-Based Simulator of Underwater Sensors for Measuring the Amount of Fish

García‐Magariño

Lacuesta

Lloret

2017

Sensors

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Underwater sensors provide one of the possibilities to explore oceans, seas, rivers, fish farms and dams, which all together cover most of our planet’s area. Simulators can be helpful to test and discover some possible strategies before implementing these in real underwater sensors. This speeds up the development of research theories so that these can be implemented later. In this context, the current work presents an agent-based simulator for defining and testing strategies for measuring the amount of fish by means of underwater sensors. The current approach is illustrated with the definition and assessment of two strategies for measuring fish. One of these two corresponds to a simple control mechanism, while the other is an experimental strategy and includes an implicit coordination mechanism. The experimental strategy showed a statistically significant improvement over the control one in the reduction of errors with a large Cohen’s d effect size of 2.55.

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“…Underwater image enhancement is usually required for analysis of the image of underwater objects [3,4]. On the other hand, imaging sonar systems often apply to fish monitoring in turbid or low ambient light waters [5][6][7][8][9]. Figure 2a depicts the underwater area covered by the imaging sonar system adopted in this paper.…”

Section: Introductionmentioning

confidence: 99%

Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields

Chang

Wang

Cheng

2021

Sensors

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Imaging sonar systems are widely used for monitoring fish behavior in turbid or low ambient light waters. For analyzing fish behavior in sonar images, fish segmentation is often required. In this paper, Mask R-CNN is adopted for segmenting fish in sonar images. Sonar images acquired from different shallow waters can be quite different in the contrast between fish and the background. That difference can make Mask R-CNN trained on examples collected from one fish farm ineffective to fish segmentation for the other fish farms. In this paper, a preprocessing convolutional neural network (PreCNN) is proposed to provide “standardized” feature maps for Mask R-CNN and to ease applying Mask R-CNN trained for one fish farm to the others. PreCNN aims at decoupling learning of fish instances from learning of fish-cultured environments. PreCNN is a semantic segmentation network and integrated with conditional random fields. PreCNN can utilize successive sonar images and can be trained by semi-supervised learning to make use of unlabeled information. Experimental results have shown that Mask R-CNN on the output of PreCNN is more accurate than Mask R-CNN directly on sonar images. Applying Mask R-CNN plus PreCNN trained for one fish farm to new fish farms is also more effective.

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Imaging sonar-based fish detection in shallow waters

Cited by 17 publications

References 7 publications

Ocean Floor Observation and Bathymetry System (OFOBS): A New Towed Camera/Sonar System for Deep-Sea Habitat Surveys

Ocean Floor Observation and Bathymetry System (OFOBS): A New Towed Camera/Sonar System for Deep-Sea Habitat Surveys

ABS-FishCount: An Agent-Based Simulator of Underwater Sensors for Measuring the Amount of Fish

Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields

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