A Review on the Use of Computer Vision and Artificial Intelligence for Fish Recognition, Monitoring, and Management

Fishes, Journal Year: 2022, Volume and Issue: 7(6), P. 335 - 335

Published: Nov. 17, 2022

Computer vision has been applied to fish recognition for at least three decades. With the inception of deep learning techniques in early 2010s, use digital images grew strongly, and this trend is likely continue. As number articles published grows, it becomes harder keep track current state art determine best course action new studies. In context, article characterizes by identifying main studies on subject briefly describing their approach. contrast with most previous reviews related technology recognition, monitoring, management, rather than providing a detailed overview being proposed, work focuses heavily challenges research gaps that still remain. Emphasis given prevalent weaknesses prevent more widespread type practical operations under real-world conditions. Some possible solutions potential directions future are suggested, as an effort bring developed academy closer meeting requirements found practice.

Language: Английский

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Applications of deep learning in fish habitat monitoring: A tutorial and survey

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 238, P. 121841 - 121841

Published: Oct. 1, 2023

Marine ecosystems and their fish habitats are becoming increasingly important due to integral role in providing a valuable food source conservation outcomes. Due remote difficult access nature, marine environments often monitored using underwater cameras record videos images for understanding life ecology, as well preserve the environment. There currently many permanent camera systems deployed at different places around globe. In addition, there exists numerous studies that use temporary survey habitats. These generate massive volume of digital data, which cannot be efficiently analysed by current manual processing methods, involve human observer. Deep Learning (DL) is cutting-edge Artificial Intelligence (AI) technology has demonstrated unprecedented performance analysing visual data. Despite its application myriad domains, habitat monitoring remains under explored. this paper, we provide tutorial covers key concepts DL, help reader grasp high-level how DL works. The also explains step-by-step procedure on algorithms should developed challenging applications such monitoring. comprehensive deep learning techniques including classification, counting, localisation, segmentation. Furthermore, publicly available datasets, compare various domains. We discuss some challenges opportunities emerging field processing. This paper written serve scientists who would like develop it following our tutorial, see evolving facilitate research efforts. At same time, suitable computer state-of-the-art DL-based methodologies

Language: Английский

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Research progress of computer vision technology in abnormal fish detection

Aquacultural Engineering, Journal Year: 2023, Volume and Issue: 103, P. 102350 - 102350

Published: Aug. 1, 2023

Language: Английский

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ConvFishNet: An efficient backbone for fish classification from composited underwater images

Information Sciences, Journal Year: 2024, Volume and Issue: 679, P. 121078 - 121078

Published: June 19, 2024

Language: Английский

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Surveying the deep: A review of computer vision in the benthos

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 102989 - 102989

Published: Jan. 1, 2025

Language: Английский

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Ecological impacts of climate change on Arctic marine megafauna

Trends in Ecology & Evolution, Journal Year: 2023, Volume and Issue: 38(8), P. 773 - 783

Published: May 16, 2023

Global warming affects the Arctic more than any other region. Mass media constantly relay apocalyptic visions of climate change threatening wildlife, especially emblematic megafauna such as polar bears, whales, and seabirds. Yet, we are just beginning to understand ecological impacts on marine at scale Arctic. This knowledge is geographically taxonomically biased, with striking deficiencies in Russian strong focus exploited species cod. Beyond a synthesis scientific advances past 5 years, provide ten key questions be addressed by future work outline requested methodology. framework builds upon long-term monitoring inclusive local communities whilst capitalising high-tech big data approaches.

Language: Английский

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Fish Detection and Classification for Automatic Sorting System with an Optimized YOLO Algorithm

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(6), P. 3812 - 3812

Published: March 16, 2023

Automatic fish recognition using deep learning and computer or machine vision is a key part of making the industry more productive through automation. An automatic sorting system will help to tackle challenges increasing food demand threat scarcity in future due continuing growth world population impact global warming climate change. As far as authors know, there has been no published work so detect classify moving for culture industry, especially purposes based on species vision. This paper proposes an approach algorithm YOLOv4, optimized with unique labeling technique. The proposed method was tested videos real running conveyor, which were put randomly position order at speed 505.08 m/h could obtain accuracy 98.15%. study simple but effective expected be guide automatically detecting, classifying, fish.

Language: Английский

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Prawn Morphometrics and Weight Estimation from Images using Deep Learning for Landmark Localization

Aquacultural Engineering, Journal Year: 2024, Volume and Issue: 106, P. 102391 - 102391

Published: Jan. 20, 2024

Language: Английский

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Live Fish Species Classification in Underwater Images by Using Convolutional Neural Networks Based on Incremental Learning with Knowledge Distillation Loss

Machine Learning and Knowledge Extraction, Journal Year: 2022, Volume and Issue: 4(3), P. 753 - 767

Published: Aug. 22, 2022

Nowadays, underwater video systems are largely used by marine ecologists to study the biodiversity in environments. These non-destructive, do not perturb environment and generate a large amount of visual data usable at any time. However, automatic analysis requires efficient techniques image processing due poor quality images challenging environment. In this paper, we address live reef fish species classification an unconstrained We propose using deep Convolutional Neural Network (CNN) training network new strategy based on incremental learning. This consists CNN progressively focusing first learning difficult well then gradually incrementally knowledge distillation loss while keeping high performances old already learned. The proposed approach yields accuracy 81.83% LifeClef 2015 Fish benchmark dataset.

Language: Английский

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Applications of machine learning to identify and characterize the sounds produced by fish

ICES Journal of Marine Science, Journal Year: 2023, Volume and Issue: 80(7), P. 1854 - 1867

Published: Aug. 11, 2023

Abstract Aquatic ecosystems are constantly changing due to anthropic stressors, which can lead biodiversity loss. Ocean sound is considered an essential ocean variable, with the potential improve our understanding of its impact on marine life. Fish produce a variety sounds and their choruses often dominate underwater soundscapes. These have been used assess communication, behaviour, spawning location, biodiversity. Artificial intelligence provide robust solution detect classify fish sounds. However, main challenge in applying artificial recognize lack validated data for individual species. This review provides overview recent publications use machine learning, including deep detection, classification, identification. Key challenges limitations discussed, some points guide future studies also provided.

Language: Английский

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