Counting Fish in Sonar Images

Liu, Liang; Lü, Hao; Xiao, Yang

doi:10.1109/icip.2018.8451154

Cited by 26 publications

(15 citation statements)

References 10 publications

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“…The problem of counting fish and estimating their size has been studied by many research groups. The problem of fish counting on a smaller scale is presented in [36][37][38][39][40]. The estimation of fish weight and its classification is studied in [41][42][43].…”

Section: Fish Population Modelingmentioning

confidence: 99%

Heterogeneous Autonomous Robotic System in Viticulture and Mariculture: Vehicles Development and Systems Integration

Kapetanović

Goričanec

Vatavuk

et al. 2022

Sensors

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There are activities in viticulture and mariculture that require extreme physical endurance from human workers, making them prime candidates for automation and robotization. This paper presents a novel, practical, heterogeneous, autonomous robotic system divided into two main parts, each dealing with respective scenarios in viticulture and mariculture. The robotic components and the subsystems that enable collaboration were developed as part of the ongoing HEKTOR project, and each specific scenario is presented. In viticulture, this includes vineyard surveillance, spraying and suckering with an all-terrain mobile manipulator (ATMM) and a lightweight autonomous aerial robot (LAAR) that can be used in very steep vineyards where other mechanization fails. In mariculture, scenarios include coordinated aerial and subsurface monitoring of fish net pens using the LAAR, an autonomous surface vehicle (ASV), and a remotely operated underwater vehicle (ROV). All robotic components communicate and coordinate their actions through the Robot Operating System (ROS). Field tests demonstrate the great capabilities of the HEKTOR system for the fully autonomous execution of very strenuous and hazardous work in viticulture and mariculture, while meeting the necessary conditions for the required quality and quantity of the work performed.

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Section: Fish Population Modelingmentioning

confidence: 99%

Heterogeneous Autonomous Robotic System in Viticulture and Mariculture: Vehicles Development and Systems Integration

Kapetanović

Goričanec

Vatavuk

et al. 2022

Sensors

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“…To monitor the states of fish in underwater areas with low optical visibility, imaging sonar systems are often without alternative. Applications of imaging sonar systems in aquaculture are broad, such as fish counting [5,[15][16][17][18][19], recording fish schools [9,20], fish tracking [21], fish detection [8,22], and monitoring of fish behavior [6,7] and feeding [23,24]. Image processing algorithms, such as adaptive thresholding and background subtraction, often apply in those applications for fish segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…In [29], CNNs are found suitable for detecting objects of known shapes on the seabed. In [18,19], CNNs are also shown to be effective in fish counting in sonar images.…”

Section: Related Workmentioning

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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“…Supervised learning is commonly used for classification and regression, where using data as a sample after trained by machine learning model which have the same target values [21]. From the theory of machine learning as well as its advantages, there are several implements in aquaculture recently such as biomass fish detection [22], size estimates [23][24][25], weight estimates [26][27][28], count [29][30][31][32], fish recognition [33][34][35][36][37][38], age detection [39,40], sex identification [34,[41][42][43], fish species classification [44][45][46][47][48][49][50], feeding behavior [51,52], group behavior [53], abnormal behavior [54,55], univariate prediction [38,[56][57][58][59], multivariate prediction [60][61][62], with the high accuracy rate.…”

Section: Introductionmentioning

confidence: 99%

Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision

Kim

2021

Electronics

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Smart aquaculture is nowadays one of the sustainable development trends for the aquaculture industry in intelligence and automation. Modern intelligent technologies have brought huge benefits to many fields including aquaculture to reduce labor, enhance aquaculture production, and be friendly to the environment. Machine learning is a subdivision of artificial intelligence (AI) by using trained algorithm models to recognize and learn traits from the data it watches. To date, there are several studies about applications of machine learning for smart aquaculture including measuring size, weight, grading, disease detection, and species classification. This review provides and overview of the development of smart aquaculture and intelligent technology. We summarized and collected 100 articles about machine learning in smart aquaculture from nearly 10 years about the methodology, results as well as the recent technology that should be used for development of smart aquaculture. We hope that this review will give readers interested in this field useful information.

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Counting Fish in Sonar Images

Cited by 26 publications

References 10 publications

Heterogeneous Autonomous Robotic System in Viticulture and Mariculture: Vehicles Development and Systems Integration

Heterogeneous Autonomous Robotic System in Viticulture and Mariculture: Vehicles Development and Systems Integration

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

Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision

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