{"id":"https://openalex.org/W3120890393","doi":"https://doi.org/10.1117/12.2587693","title":"An instance segmentation framework for in-situ plankton taxa assessment","display_name":"An instance segmentation framework for in-situ plankton taxa assessment","publication_year":2021,"publication_date":"2021-01-04","ids":{"openalex":"https://openalex.org/W3120890393","doi":"https://doi.org/10.1117/12.2587693","mag":"3120890393"},"language":"en","primary_location":{"id":"doi:10.1117/12.2587693","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2587693","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Thirteenth International Conference on Machine Vision","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034048112","display_name":"Aya Saad","orcid":"https://orcid.org/0000-0003-0198-8594"},"institutions":[{"id":"https://openalex.org/I204778367","display_name":"Norwegian University of Science and Technology","ror":"https://ror.org/05xg72x27","country_code":"NO","type":"education","lineage":["https://openalex.org/I204778367"]}],"countries":["NO"],"is_corresponding":true,"raw_author_name":"Aya Saad","raw_affiliation_strings":["Norwegian Univ. of Science and Technology (Norway)"],"affiliations":[{"raw_affiliation_string":"Norwegian Univ. of Science and Technology (Norway)","institution_ids":["https://openalex.org/I204778367"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067001790","display_name":"Sondre Bergrum","orcid":null},"institutions":[{"id":"https://openalex.org/I204778367","display_name":"Norwegian University of Science and Technology","ror":"https://ror.org/05xg72x27","country_code":"NO","type":"education","lineage":["https://openalex.org/I204778367"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Sondre Bergrum","raw_affiliation_strings":["Norwegian Univ. of Science and Technology (Norway)"],"affiliations":[{"raw_affiliation_string":"Norwegian Univ. of Science and Technology (Norway)","institution_ids":["https://openalex.org/I204778367"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033793373","display_name":"Annette Stahl","orcid":"https://orcid.org/0000-0002-8422-1091"},"institutions":[{"id":"https://openalex.org/I204778367","display_name":"Norwegian University of Science and Technology","ror":"https://ror.org/05xg72x27","country_code":"NO","type":"education","lineage":["https://openalex.org/I204778367"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Annette Stahl","raw_affiliation_strings":["Norwegian Univ. of Science and Technology (Norway)"],"affiliations":[{"raw_affiliation_string":"Norwegian Univ. of Science and Technology (Norway)","institution_ids":["https://openalex.org/I204778367"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5034048112"],"corresponding_institution_ids":["https://openalex.org/I204778367"],"apc_list":null,"apc_paid":null,"fwci":0.2488,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.53628021,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"78","last_page":"78"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10032","display_name":"Marine and coastal ecosystems","score":0.9718000292778015,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10255","display_name":"Oceanographic and Atmospheric Processes","score":0.9538000226020813,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.817868173122406},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.764460027217865},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.710104763507843},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5046194791793823},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5028275847434998},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.42884087562561035},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.4144662320613861},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35327959060668945}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.817868173122406},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.764460027217865},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.710104763507843},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5046194791793823},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5028275847434998},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.42884087562561035},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.4144662320613861},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35327959060668945},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2587693","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2587693","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Thirteenth International Conference on Machine Vision","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life below water","id":"https://metadata.un.org/sdg/14","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2378076731","https://openalex.org/W4375867731","https://openalex.org/W4286888643","https://openalex.org/W3210795196","https://openalex.org/W2088988140","https://openalex.org/W2103019253","https://openalex.org/W2951529875","https://openalex.org/W2391453316","https://openalex.org/W4380075502","https://openalex.org/W1522196789"],"abstract_inverted_index":{"In":[0,187],"this":[1,140],"paper,":[2],"we":[3,96,189,200],"propose":[4],"a":[5,126,191,246],"deep":[6,158],"learning":[7,159],"instance":[8,89,120,145,227],"segmentation":[9,179,185,228],"framework":[10,28,68],"for":[11,88,116],"particle":[12,233],"extraction":[13],"of":[14,47,100,119,150,172,176,210,251],"microscopic":[15],"images":[16,37,133],"that":[17],"aims":[18],"at":[19],"calculating":[20],"planktonic":[21,62,131,252],"species":[22],"distribution":[23,60,235],"and":[24,55,57,61,121,134,174,180,236,255],"concentration":[25,64],"in-situ.":[26],"The":[27,221],"comprises":[29],"three":[30],"essential":[31],"functional":[32],"tasks":[33],"on":[34,71],"in-situ":[35,178],"time-series":[36,132],"collected":[38],"from":[39,130,217,224],"an":[40],"autonomous":[41],"underwater":[42],"vehicle:":[43],"1)":[44],"manual":[45],"labeling":[46],"the":[48,72,78,98,105,112,117,136,144,151,155,163,195,202,214,218,232],"captured":[49],"images,":[50],"2)":[51],"object":[52,196],"localization,":[53],"segmentation,":[54],"identification,":[56],"3)":[58],"class":[59,234],"organisms":[63],"calculation.":[65],"Our":[66],"proposed":[67,152],"is":[69],"based":[70],"mask":[73],"R-CNN":[74],"architecture":[75,115,160],"provided":[76],"by":[77,82,103],"Detectron2":[79],"library":[80],"developed":[81],"Facebook":[83],"Artificial":[84],"Intelligence":[85],"Research":[86],"(FAIR)":[87],"segmentation.":[90,123,147],"Due":[91],"to":[92,110,142,183,242],"its":[93],"modular":[94],"design,":[95],"compare":[97],"performance":[99,169],"different":[101,137],"networks":[102],"alternating":[104],"backbone":[106],"sub-network":[107],"in":[108,170,194,244],"order":[109],"choose":[111],"most":[113],"suitable":[114],"task":[118],"semantic":[122,146],"We":[124],"compile":[125],"custom":[127],"annotated":[128,165,207],"dataset":[129,141,208,215],"train":[135,201],"models":[138],"over":[139,204,213],"perform":[143],"Evaluation":[148],"results":[149],"framework,":[153,229],"utilizing":[154],"best":[156],"performing":[157],"along":[161],"with":[162],"new":[164],"dataset,":[166],"show":[167],"better":[168],"terms":[171],"speed":[173],"accuracy":[175],"both":[177],"classification":[181,197],"compared":[182],"traditional":[184,219],"methods.":[186,220],"addition,":[188],"observe":[190],"significant":[192],"improvement":[193],"quality":[198],"when":[199],"model":[203],"our":[205,225],"newly":[206],"instead":[209],"training":[211],"it":[212],"generated":[216],"inferred":[222],"data":[223],"novel":[226],"which":[230],"provides":[231],"concentration,":[237],"can":[238],"then":[239],"be":[240],"used":[241],"assist":[243],"constructing":[245],"dynamic":[247],"probability":[248],"density":[249],"map":[250],"communities":[253],"dispersion":[254],"abundance.":[256]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}