The Application of Deep Learning in Marine Sciences

Martin‐Abadal, Miguel; Ruiz‐Frau, Ana; Hinz, Hilmar; González-Cid, Yolanda

doi:10.1007/978-3-030-31760-7_7

Cited by 2 publications

(2 citation statements)

References 55 publications

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“…The measure of abiotic features facilitates upscaling of ecological studies at scales relevant to ecosystem functioning (Schenone et al, 2021 ). Computer Vision can be applied to automate the processing of vast amounts of information resulting from the remote and continuous acquisition of visual data in a cost‐effective and a high precision manner (Azhar et al, 2020 ; Martin‐Abadal et al, 2020 ).…”

Section: New Paths: On Solutions To Advance the Bta Approachmentioning

confidence: 99%

Biological traits approaches in benthic marine ecology: Dead ends and new paths

Juan

Bremner

Hewitt

et al. 2022

Ecology and Evolution

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Biological traits analysis (BTA) links community structure to both ecological functions and response to environmental drivers through species’ attributes. In consequence, it has become a popular approach in marine benthic studies. However, BTA will reach a dead end if the scientific community does not acknowledge its current shortcomings and limitations: (a) uncertainties related to data origins and a lack of standardized reporting of trait information; (b) knowledge gaps on the role of multiple interacting traits on driving the organisms’ responses to environmental variability; (c) knowledge gaps regarding the mechanistic links between traits and functions; (d) a weak focus on the spatial and temporal variability that is inherent to the trait expression of species; and, last but not least, (e) the large reliance on expert knowledge due to an enormous knowledge gap on the basic ecology of many benthic species. BTA will only reach its full potential if the scientific community is able to standardize and unify the reporting and storage of traits data and reconsider the importance of baseline observational and experimental studies to fill knowledge gaps on the mechanistic links between biological traits, functions, and environmental variability. This challenge could be assisted by embracing new technological advances in marine monitoring, such as underwater camera technology and artificial intelligence, and making use of advanced statistical approaches that consider the interactive nature and spatio‐temporal variability of biological systems. The scientific community has to abandon some dead ends and explore new paths that will improve our understanding of individual species, traits, and the functioning of benthic ecosystems.

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Section: New Paths: On Solutions To Advance the Bta Approachmentioning

confidence: 99%

Biological traits approaches in benthic marine ecology: Dead ends and new paths

Juan

Bremner

Hewitt

et al. 2022

Ecology and Evolution

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“…The development of intelligent robots has been widely applied across various fields, including target detection and adaptive control (Martin-Abadal et al, 2020). In tasks such as marine exploration and rescue missions, detecting sea Jellyfish stings is crucial due to the threat they pose to human health.…”

Section: Introductionmentioning

confidence: 99%

Real-time precision detection algorithm for jellyfish stings in neural computing, featuring adaptive deep learning enhanced by an advanced YOLOv4 framework

Zhu,

Feng,

2024

Front. Neurorobot.

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IntroductionSea jellyfish stings pose a threat to human health, and traditional detection methods face challenges in terms of accuracy and real-time capabilities.MethodsTo address this, we propose a novel algorithm that integrates YOLOv4 object detection, an attention mechanism, and PID control. We enhance YOLOv4 to improve the accuracy and real-time performance of detection. Additionally, we introduce an attention mechanism to automatically focus on critical areas of sea jellyfish stings, enhancing detection precision. Ultimately, utilizing the PID control algorithm, we achieve adaptive adjustments in the robot's movements and posture based on the detection results. Extensive experimental evaluations using a real sea jellyfish sting image dataset demonstrate significant improvements in accuracy and real-time performance using our proposed algorithm. Compared to traditional methods, our algorithm more accurately detects sea jellyfish stings and dynamically adjusts the robot's actions in real-time, maximizing protection for human health.Results and discussionThe significance of this research lies in providing an efficient and accurate sea jellyfish sting detection algorithm for intelligent robot systems. The algorithm exhibits notable improvements in real-time capabilities and precision, aiding robot systems in better identifying and addressing sea jellyfish stings, thereby safeguarding human health. Moreover, the algorithm possesses a certain level of generality and can be applied to other applications in target detection and adaptive control, offering broad prospects for diverse applications.

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The Application of Deep Learning in Marine Sciences

Cited by 2 publications

References 55 publications

Biological traits approaches in benthic marine ecology: Dead ends and new paths

Biological traits approaches in benthic marine ecology: Dead ends and new paths

Real-time precision detection algorithm for jellyfish stings in neural computing, featuring adaptive deep learning enhanced by an advanced YOLOv4 framework

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