Automated Detection, Classification and Counting of Fish in Fish Passages With Deep Learning Article Swipe
Vishnu Kandimalla
,
Matt Richard
,
Frank H. Smith
,
Jean Quirion
,
Luı́s Torgo
,
Chris Whidden
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fmars.2021.823173
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fmars.2021.823173
The Ocean Aware project, led by Innovasea and funded through Canada's Ocean Supercluster, is developing a fish passage observation platform to monitor fish without the use of traditional tags. This will provide an alternative to standard tracking technology, such as acoustic telemetry fish tracking, which are often not appropriate for tracking at-risk fish species protected by legislation. Rather, the observation platform uses a combination of sensors including acoustic devices, visual and active sonar, and optical cameras. This will enable more in-depth scientific research and better support regulatory monitoring of at-risk fish species in fish passages or marine energy sites. Analysis of this data will require a robust and accurate method to automatically detect fish, count fish, and classify them by species in real-time using both sonar and optical cameras. To meet this need, we developed and tested an automated real-time deep learning framework combining state of the art convolutional neural networks and Kalman filters. First, we showed that an adaptation of the widely used YOLO machine learning model can accurately detect and classify eight species of fish from a public high resolution DIDSON imaging sonar dataset captured from the Ocqueoc River in Michigan, USA. Although there has been extensive research in the literature identifying particular fish such as eel vs. non-eel and seal vs. fish, to our knowledge this is the first successful application of deep learning for classifying multiple fish species with high resolution imaging sonar. Second, we integrated the Norfair object tracking framework to track and count fish using a public video dataset captured by optical cameras from the Wells Dam fish ladder on the Columbia River in Washington State, USA. Our results demonstrate that deep learning models can indeed be used to detect, classify species, and track fish using both high resolution imaging sonar and underwater video from a fish ladder. This work is a first step toward developing a fully implemented system which can accurately detect, classify and generate insights about fish in a wide variety of fish passage environments and conditions with data collected from multiple types of sensors.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fmars.2021.823173
- https://www.frontiersin.org/articles/10.3389/fmars.2021.823173/pdf
- OA Status
- gold
- Cited By
- 99
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4205761787
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4205761787Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fmars.2021.823173Digital Object Identifier
- Title
-
Automated Detection, Classification and Counting of Fish in Fish Passages With Deep LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-13Full publication date if available
- Authors
-
Vishnu Kandimalla, Matt Richard, Frank H. Smith, Jean Quirion, Luı́s Torgo, Chris WhiddenList of authors in order
- Landing page
-
https://doi.org/10.3389/fmars.2021.823173Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fmars.2021.823173/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/articles/10.3389/fmars.2021.823173/pdfDirect OA link when available
- Concepts
-
Convolutional neural network, Sonar, Computer science, Artificial intelligence, Adaptation (eye), Fish
, Citizen science, Fishery, Biology, Neuroscience, Botany Top concepts (fields/topics) attached by OpenAlex - Cited by
-
99Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 28, 2024: 35, 2023: 29, 2022: 7Per-year citation counts (last 5 years)
- References (count)
-
52Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4205761787 |
|---|---|
| doi | https://doi.org/10.3389/fmars.2021.823173 |
| ids.doi | https://doi.org/10.3389/fmars.2021.823173 |
| ids.openalex | https://openalex.org/W4205761787 |
| fwci | 36.96872318 |
| type | article |
| title | Automated Detection, Classification and Counting of Fish in Fish Passages With Deep Learning |
| awards[0].id | https://openalex.org/G627012136 |
| awards[0].funder_id | https://openalex.org/F4320322675 |
| awards[0].display_name | |
| awards[0].funder_award_id | IT22090 |
| awards[0].funder_display_name | Mitacs |
| biblio.issue | |
| biblio.volume | 8 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10302 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9988999962806702 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2309 |
| topics[0].subfield.display_name | Nature and Landscape Conservation |
| topics[0].display_name | Fish Ecology and Management Studies |
| topics[1].id | https://openalex.org/T12388 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9965999722480774 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1312 |
| topics[1].subfield.display_name | Molecular Biology |
| topics[1].display_name | Identification and Quantification in Food |
| topics[2].id | https://openalex.org/T12697 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9923999905586243 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2312 |
| topics[2].subfield.display_name | Water Science and Technology |
| topics[2].display_name | Water Quality Monitoring Technologies |
| funders[0].id | https://openalex.org/F4320322675 |
| funders[0].ror | https://ror.org/00cjrc276 |
| funders[0].display_name | Mitacs |
| funders[1].id | https://openalex.org/F4320334593 |
| funders[1].ror | https://ror.org/01h531d29 |
| funders[1].display_name | Natural Sciences and Engineering Research Council of Canada |
| is_xpac | False |
| apc_list.value | 2950 |
| apc_list.currency | USD |
| apc_list.value_usd | 2950 |
| apc_paid.value | 2950 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 2950 |
| concepts[0].id | https://openalex.org/C81363708 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6483522057533264 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[0].display_name | Convolutional neural network |
| concepts[1].id | https://openalex.org/C555745239 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6231997609138489 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q133220 |
| concepts[1].display_name | Sonar |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.595621645450592 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5240014791488647 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C139807058 |
| concepts[4].level | 2 |
| concepts[4].score | 0.43758174777030945 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q352374 |
| concepts[4].display_name | Adaptation (eye) |
| concepts[5].id | https://openalex.org/C2909208804 |
| concepts[5].level | 2 |
| concepts[5].score | 0.41470834612846375 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q127282 |
| concepts[5].display_name | Fish |
| concepts[6].id | https://openalex.org/C197352329 |
| concepts[6].level | 2 |
| concepts[6].score | 0.41064873337745667 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1093434 |
| concepts[6].display_name | Citizen science |
| concepts[7].id | https://openalex.org/C505870484 |
| concepts[7].level | 1 |
| concepts[7].score | 0.347400039434433 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q180538 |
| concepts[7].display_name | Fishery |
| concepts[8].id | https://openalex.org/C86803240 |
| concepts[8].level | 0 |
| concepts[8].score | 0.12234866619110107 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[8].display_name | Biology |
| concepts[9].id | https://openalex.org/C169760540 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[9].display_name | Neuroscience |
| concepts[10].id | https://openalex.org/C59822182 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q441 |
| concepts[10].display_name | Botany |
| keywords[0].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[0].score | 0.6483522057533264 |
| keywords[0].display_name | Convolutional neural network |
| keywords[1].id | https://openalex.org/keywords/sonar |
| keywords[1].score | 0.6231997609138489 |
| keywords[1].display_name | Sonar |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.595621645450592 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.5240014791488647 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/adaptation |
| keywords[4].score | 0.43758174777030945 |
| keywords[4].display_name | Adaptation (eye) |
| keywords[5].id | https://openalex.org/keywords/fish-actinopterygii |
| keywords[5].score | 0.41470834612846375 |
| keywords[5].display_name | Fish |
| keywords[6].id | https://openalex.org/keywords/citizen-science |
| keywords[6].score | 0.41064873337745667 |
| keywords[6].display_name | Citizen science |
| keywords[7].id | https://openalex.org/keywords/fishery |
| keywords[7].score | 0.347400039434433 |
| keywords[7].display_name | Fishery |
| keywords[8].id | https://openalex.org/keywords/biology |
| keywords[8].score | 0.12234866619110107 |
| keywords[8].display_name | Biology |
| language | en |
| locations[0].id | doi:10.3389/fmars.2021.823173 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2596539584 |
| locations[0].source.issn | 2296-7745 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2296-7745 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Frontiers in Marine Science |
| locations[0].source.host_organization | https://openalex.org/P4310320527 |
| locations[0].source.host_organization_name | Frontiers Media |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320527 |
| locations[0].source.host_organization_lineage_names | Frontiers Media |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.frontiersin.org/articles/10.3389/fmars.2021.823173/pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Frontiers in Marine Science |
| locations[0].landing_page_url | https://doi.org/10.3389/fmars.2021.823173 |
| locations[1].id | pmh:oai:doaj.org/article:4bd7cbf70a3f4f588298ca38ccedf21b |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | cc-by-sa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Frontiers in Marine Science, Vol 8 (2022) |
| locations[1].landing_page_url | https://doaj.org/article/4bd7cbf70a3f4f588298ca38ccedf21b |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5011735043 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Vishnu Kandimalla |
| authorships[0].countries | CA |
| authorships[0].affiliations[0].raw_affiliation_string | Innovasea, Halifax, NS, Canada |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I129902397 |
| authorships[0].affiliations[1].raw_affiliation_string | Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada |
| authorships[0].institutions[0].id | https://openalex.org/I129902397 |
| authorships[0].institutions[0].ror | https://ror.org/01e6qks80 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I129902397 |
| authorships[0].institutions[0].country_code | CA |
| authorships[0].institutions[0].display_name | Dalhousie University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Vishnu Kandimalla |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada, Innovasea, Halifax, NS, Canada |
| authorships[1].author.id | https://openalex.org/A5081388923 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Matt Richard |
| authorships[1].affiliations[0].raw_affiliation_string | Innovasea, Halifax, NS, Canada |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Matt Richard |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Innovasea, Halifax, NS, Canada |
| authorships[2].author.id | https://openalex.org/A5111918744 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Frank H. Smith |
| authorships[2].affiliations[0].raw_affiliation_string | Innovasea, Halifax, NS, Canada |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Frank Smith |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Innovasea, Halifax, NS, Canada |
| authorships[3].author.id | https://openalex.org/A5047670201 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-0036-2813 |
| authorships[3].author.display_name | Jean Quirion |
| authorships[3].affiliations[0].raw_affiliation_string | Innovasea, Halifax, NS, Canada |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Jean Quirion |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Innovasea, Halifax, NS, Canada |
| authorships[4].author.id | https://openalex.org/A5080727549 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-6892-8871 |
| authorships[4].author.display_name | Luı́s Torgo |
| authorships[4].countries | CA |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I129902397 |
| authorships[4].affiliations[0].raw_affiliation_string | Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada |
| authorships[4].institutions[0].id | https://openalex.org/I129902397 |
| authorships[4].institutions[0].ror | https://ror.org/01e6qks80 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I129902397 |
| authorships[4].institutions[0].country_code | CA |
| authorships[4].institutions[0].display_name | Dalhousie University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Luis Torgo |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada |
| authorships[5].author.id | https://openalex.org/A5041594411 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-9406-730X |
| authorships[5].author.display_name | Chris Whidden |
| authorships[5].countries | CA |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I129902397 |
| authorships[5].affiliations[0].raw_affiliation_string | Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada |
| authorships[5].institutions[0].id | https://openalex.org/I129902397 |
| authorships[5].institutions[0].ror | https://ror.org/01e6qks80 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I129902397 |
| authorships[5].institutions[0].country_code | CA |
| authorships[5].institutions[0].display_name | Dalhousie University |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Chris Whidden |
| authorships[5].is_corresponding | True |
| authorships[5].raw_affiliation_strings | Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.frontiersin.org/articles/10.3389/fmars.2021.823173/pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Automated Detection, Classification and Counting of Fish in Fish Passages With Deep Learning |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10302 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9988999962806702 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2309 |
| primary_topic.subfield.display_name | Nature and Landscape Conservation |
| primary_topic.display_name | Fish Ecology and Management Studies |
| related_works | https://openalex.org/W2134020923, https://openalex.org/W2479871838, https://openalex.org/W3199220297, https://openalex.org/W4379468622, https://openalex.org/W2093540596, https://openalex.org/W2137769200, https://openalex.org/W2792023157, https://openalex.org/W2032962400, https://openalex.org/W2952608638, https://openalex.org/W1815107807 |
| cited_by_count | 99 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 28 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 35 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 29 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 7 |
| locations_count | 2 |
| best_oa_location.id | doi:10.3389/fmars.2021.823173 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2596539584 |
| best_oa_location.source.issn | 2296-7745 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2296-7745 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Frontiers in Marine Science |
| best_oa_location.source.host_organization | https://openalex.org/P4310320527 |
| best_oa_location.source.host_organization_name | Frontiers Media |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320527 |
| best_oa_location.source.host_organization_lineage_names | Frontiers Media |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.frontiersin.org/articles/10.3389/fmars.2021.823173/pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Frontiers in Marine Science |
| best_oa_location.landing_page_url | https://doi.org/10.3389/fmars.2021.823173 |
| primary_location.id | doi:10.3389/fmars.2021.823173 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2596539584 |
| primary_location.source.issn | 2296-7745 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2296-7745 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Frontiers in Marine Science |
| primary_location.source.host_organization | https://openalex.org/P4310320527 |
| primary_location.source.host_organization_name | Frontiers Media |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320527 |
| primary_location.source.host_organization_lineage_names | Frontiers Media |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.frontiersin.org/articles/10.3389/fmars.2021.823173/pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Frontiers in Marine Science |
| primary_location.landing_page_url | https://doi.org/10.3389/fmars.2021.823173 |
| publication_date | 2022-01-13 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2518134063, https://openalex.org/W2739651313, https://openalex.org/W6770924551, https://openalex.org/W2252355370, https://openalex.org/W2980105807, https://openalex.org/W6777046832, https://openalex.org/W2955303978, https://openalex.org/W6794655914, https://openalex.org/W6763930870, https://openalex.org/W6631782140, https://openalex.org/W6735463952, https://openalex.org/W2109255472, https://openalex.org/W6687483927, https://openalex.org/W6703518758, https://openalex.org/W2999917256, https://openalex.org/W3014286955, https://openalex.org/W2222512263, https://openalex.org/W2919115771, https://openalex.org/W2783994971, https://openalex.org/W6730903564, https://openalex.org/W6639102338, https://openalex.org/W6749783731, https://openalex.org/W2060005852, https://openalex.org/W2897327260, https://openalex.org/W2104068357, https://openalex.org/W2962778460, https://openalex.org/W3043995050, https://openalex.org/W3017014433, https://openalex.org/W2963246747, https://openalex.org/W6628973269, https://openalex.org/W6750227808, https://openalex.org/W639708223, https://openalex.org/W2117539524, https://openalex.org/W1990054309, https://openalex.org/W6712737577, https://openalex.org/W6679214204, https://openalex.org/W2766648222, https://openalex.org/W2956232138, https://openalex.org/W3033927182, https://openalex.org/W2076900591, https://openalex.org/W3047680022, https://openalex.org/W2891182582, https://openalex.org/W6771154167, https://openalex.org/W2603203130, https://openalex.org/W6675864661, https://openalex.org/W6755742454, https://openalex.org/W2991468593, https://openalex.org/W4251620351, https://openalex.org/W4301409532, https://openalex.org/W4234552385, https://openalex.org/W2597507805, https://openalex.org/W3104218139 |
| referenced_works_count | 52 |
| abstract_inverted_index.a | 15, 62, 105, 178, 251, 301, 307, 312, 327 |
| abstract_inverted_index.To | 129 |
| abstract_inverted_index.an | 32, 137, 158 |
| abstract_inverted_index.as | 39, 207 |
| abstract_inverted_index.be | 282 |
| abstract_inverted_index.by | 5, 55, 119, 256 |
| abstract_inverted_index.in | 92, 121, 191, 200, 269, 326 |
| abstract_inverted_index.is | 13, 219, 306 |
| abstract_inverted_index.of | 26, 64, 88, 100, 145, 160, 175, 224, 330, 342 |
| abstract_inverted_index.on | 265 |
| abstract_inverted_index.or | 95 |
| abstract_inverted_index.to | 20, 34, 110, 215, 245, 284 |
| abstract_inverted_index.we | 133, 155, 238 |
| abstract_inverted_index.Dam | 262 |
| abstract_inverted_index.Our | 273 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.and | 7, 70, 73, 83, 107, 116, 126, 135, 151, 171, 211, 247, 288, 297, 321, 334 |
| abstract_inverted_index.are | 45 |
| abstract_inverted_index.art | 147 |
| abstract_inverted_index.can | 168, 280, 317 |
| abstract_inverted_index.eel | 208 |
| abstract_inverted_index.for | 49, 227 |
| abstract_inverted_index.has | 196 |
| abstract_inverted_index.led | 4 |
| abstract_inverted_index.not | 47 |
| abstract_inverted_index.our | 216 |
| abstract_inverted_index.the | 24, 58, 146, 161, 188, 201, 220, 240, 260, 266 |
| abstract_inverted_index.use | 25 |
| abstract_inverted_index.vs. | 209, 213 |
| abstract_inverted_index.This | 29, 76, 304 |
| abstract_inverted_index.USA. | 193, 272 |
| abstract_inverted_index.YOLO | 164 |
| abstract_inverted_index.been | 197 |
| abstract_inverted_index.both | 124, 292 |
| abstract_inverted_index.data | 102, 337 |
| abstract_inverted_index.deep | 140, 225, 277 |
| abstract_inverted_index.fish | 16, 22, 42, 52, 90, 93, 176, 205, 230, 249, 263, 290, 302, 325, 331 |
| abstract_inverted_index.from | 177, 187, 259, 300, 339 |
| abstract_inverted_index.high | 180, 233, 293 |
| abstract_inverted_index.meet | 130 |
| abstract_inverted_index.more | 79 |
| abstract_inverted_index.seal | 212 |
| abstract_inverted_index.step | 309 |
| abstract_inverted_index.such | 38, 206 |
| abstract_inverted_index.that | 157, 276 |
| abstract_inverted_index.them | 118 |
| abstract_inverted_index.this | 101, 131, 218 |
| abstract_inverted_index.used | 163, 283 |
| abstract_inverted_index.uses | 61 |
| abstract_inverted_index.wide | 328 |
| abstract_inverted_index.will | 30, 77, 103 |
| abstract_inverted_index.with | 232, 336 |
| abstract_inverted_index.work | 305 |
| abstract_inverted_index.Aware | 2 |
| abstract_inverted_index.Ocean | 1, 11 |
| abstract_inverted_index.River | 190, 268 |
| abstract_inverted_index.Wells | 261 |
| abstract_inverted_index.about | 324 |
| abstract_inverted_index.count | 114, 248 |
| abstract_inverted_index.eight | 173 |
| abstract_inverted_index.first | 221, 308 |
| abstract_inverted_index.fish, | 113, 115, 214 |
| abstract_inverted_index.fully | 313 |
| abstract_inverted_index.model | 167 |
| abstract_inverted_index.need, | 132 |
| abstract_inverted_index.often | 46 |
| abstract_inverted_index.sonar | 125, 184, 296 |
| abstract_inverted_index.state | 144 |
| abstract_inverted_index.tags. | 28 |
| abstract_inverted_index.there | 195 |
| abstract_inverted_index.track | 246, 289 |
| abstract_inverted_index.types | 341 |
| abstract_inverted_index.using | 123, 250, 291 |
| abstract_inverted_index.video | 253, 299 |
| abstract_inverted_index.which | 44, 316 |
| abstract_inverted_index.DIDSON | 182 |
| abstract_inverted_index.First, | 154 |
| abstract_inverted_index.Kalman | 152 |
| abstract_inverted_index.State, | 271 |
| abstract_inverted_index.active | 71 |
| abstract_inverted_index.better | 84 |
| abstract_inverted_index.detect | 112, 170 |
| abstract_inverted_index.enable | 78 |
| abstract_inverted_index.energy | 97 |
| abstract_inverted_index.funded | 8 |
| abstract_inverted_index.indeed | 281 |
| abstract_inverted_index.ladder | 264 |
| abstract_inverted_index.marine | 96 |
| abstract_inverted_index.method | 109 |
| abstract_inverted_index.models | 279 |
| abstract_inverted_index.neural | 149 |
| abstract_inverted_index.object | 242 |
| abstract_inverted_index.public | 179, 252 |
| abstract_inverted_index.robust | 106 |
| abstract_inverted_index.showed | 156 |
| abstract_inverted_index.sites. | 98 |
| abstract_inverted_index.sonar, | 72 |
| abstract_inverted_index.sonar. | 236 |
| abstract_inverted_index.system | 315 |
| abstract_inverted_index.tested | 136 |
| abstract_inverted_index.toward | 310 |
| abstract_inverted_index.visual | 69 |
| abstract_inverted_index.widely | 162 |
| abstract_inverted_index.Norfair | 241 |
| abstract_inverted_index.Ocqueoc | 189 |
| abstract_inverted_index.Rather, | 57 |
| abstract_inverted_index.Second, | 237 |
| abstract_inverted_index.at-risk | 51, 89 |
| abstract_inverted_index.cameras | 258 |
| abstract_inverted_index.dataset | 185, 254 |
| abstract_inverted_index.detect, | 285, 319 |
| abstract_inverted_index.imaging | 183, 235, 295 |
| abstract_inverted_index.ladder. | 303 |
| abstract_inverted_index.machine | 165 |
| abstract_inverted_index.monitor | 21 |
| abstract_inverted_index.non-eel | 210 |
| abstract_inverted_index.optical | 74, 127, 257 |
| abstract_inverted_index.passage | 17, 332 |
| abstract_inverted_index.provide | 31 |
| abstract_inverted_index.require | 104 |
| abstract_inverted_index.results | 274 |
| abstract_inverted_index.sensors | 65 |
| abstract_inverted_index.species | 53, 91, 120, 174, 231 |
| abstract_inverted_index.support | 85 |
| abstract_inverted_index.through | 9 |
| abstract_inverted_index.variety | 329 |
| abstract_inverted_index.without | 23 |
| abstract_inverted_index.Although | 194 |
| abstract_inverted_index.Analysis | 99 |
| abstract_inverted_index.Canada's | 10 |
| abstract_inverted_index.Columbia | 267 |
| abstract_inverted_index.accurate | 108 |
| abstract_inverted_index.acoustic | 40, 67 |
| abstract_inverted_index.cameras. | 75, 128 |
| abstract_inverted_index.captured | 186, 255 |
| abstract_inverted_index.classify | 117, 172, 286, 320 |
| abstract_inverted_index.devices, | 68 |
| abstract_inverted_index.filters. | 153 |
| abstract_inverted_index.generate | 322 |
| abstract_inverted_index.in-depth | 80 |
| abstract_inverted_index.insights | 323 |
| abstract_inverted_index.learning | 141, 166, 226, 278 |
| abstract_inverted_index.multiple | 229, 340 |
| abstract_inverted_index.networks | 150 |
| abstract_inverted_index.passages | 94 |
| abstract_inverted_index.platform | 19, 60 |
| abstract_inverted_index.project, | 3 |
| abstract_inverted_index.research | 82, 199 |
| abstract_inverted_index.sensors. | 343 |
| abstract_inverted_index.species, | 287 |
| abstract_inverted_index.standard | 35 |
| abstract_inverted_index.tracking | 36, 50, 243 |
| abstract_inverted_index.Innovasea | 6 |
| abstract_inverted_index.Michigan, | 192 |
| abstract_inverted_index.automated | 138 |
| abstract_inverted_index.collected | 338 |
| abstract_inverted_index.combining | 143 |
| abstract_inverted_index.developed | 134 |
| abstract_inverted_index.extensive | 198 |
| abstract_inverted_index.framework | 142, 244 |
| abstract_inverted_index.including | 66 |
| abstract_inverted_index.knowledge | 217 |
| abstract_inverted_index.protected | 54 |
| abstract_inverted_index.real-time | 122, 139 |
| abstract_inverted_index.telemetry | 41 |
| abstract_inverted_index.tracking, | 43 |
| abstract_inverted_index.Washington | 270 |
| abstract_inverted_index.accurately | 169, 318 |
| abstract_inverted_index.adaptation | 159 |
| abstract_inverted_index.conditions | 335 |
| abstract_inverted_index.developing | 14, 311 |
| abstract_inverted_index.integrated | 239 |
| abstract_inverted_index.literature | 202 |
| abstract_inverted_index.monitoring | 87 |
| abstract_inverted_index.particular | 204 |
| abstract_inverted_index.regulatory | 86 |
| abstract_inverted_index.resolution | 181, 234, 294 |
| abstract_inverted_index.scientific | 81 |
| abstract_inverted_index.successful | 222 |
| abstract_inverted_index.underwater | 298 |
| abstract_inverted_index.alternative | 33 |
| abstract_inverted_index.application | 223 |
| abstract_inverted_index.appropriate | 48 |
| abstract_inverted_index.classifying | 228 |
| abstract_inverted_index.combination | 63 |
| abstract_inverted_index.demonstrate | 275 |
| abstract_inverted_index.identifying | 203 |
| abstract_inverted_index.implemented | 314 |
| abstract_inverted_index.observation | 18, 59 |
| abstract_inverted_index.technology, | 37 |
| abstract_inverted_index.traditional | 27 |
| abstract_inverted_index.environments | 333 |
| abstract_inverted_index.legislation. | 56 |
| abstract_inverted_index.Supercluster, | 12 |
| abstract_inverted_index.automatically | 111 |
| abstract_inverted_index.convolutional | 148 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5041594411 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I129902397 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.8299999833106995 |
| sustainable_development_goals[0].display_name | Life below water |
| citation_normalized_percentile.value | 0.99837722 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |