Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

Clicks: 365
ID: 267453
2020
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #177 of 1,694 articles by views in sensors

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,694 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric info …
Reference Key
v2020sensorsvideo Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lopez-Vazquez V;Lopez-Guede JM;Marini S;Fanelli E;Johnsen E;Aguzzi J;;
Journal sensors
Year 2020
DOI
DOI not found
URL
https://www.ncbi.nlm.nih.gov/pubmed/32012976
Keywords
deep learning neural networks Computer National Center for Biotechnology Information NCBI NLM MEDLINE animals humans pubmed abstract nih national institutes of health national library of medicine image enhancement / methods ecosystem* oceans and seas biodiversity* pmid:32012976 pmc7038495 doi:10.3390/s20030726 vanesa lopez-vazquez jose manuel lopez-guede jacopo aguzzi aquatic organisms / classification aquatic organisms / physiology* machine learning video recording / methods*

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.