{"id":"https://openalex.org/W3202377682","doi":"https://doi.org/10.1145/3472634.3474302","title":"Deep sea nodule mineral image segmentation algorithm based on Mask R-CNN","display_name":"Deep sea nodule mineral image segmentation algorithm based on Mask R-CNN","publication_year":2021,"publication_date":"2021-07-30","ids":{"openalex":"https://openalex.org/W3202377682","doi":"https://doi.org/10.1145/3472634.3474302","mag":"3202377682"},"language":"en","primary_location":{"id":"doi:10.1145/3472634.3474302","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3472634.3474302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Turing Award Celebration Conference - China ( ACM TURC 2021)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5025852767","display_name":"Lihui Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I145897649","display_name":"Minzu University of China","ror":"https://ror.org/0044e2g62","country_code":"CN","type":"education","lineage":["https://openalex.org/I145897649"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lihui Dong","raw_affiliation_strings":["Minzu University of China, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Minzu University of China, China","institution_ids":["https://openalex.org/I145897649"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101749192","display_name":"Haolin Wang","orcid":"https://orcid.org/0000-0002-0477-4973"},"institutions":[{"id":"https://openalex.org/I145897649","display_name":"Minzu University of China","ror":"https://ror.org/0044e2g62","country_code":"CN","type":"education","lineage":["https://openalex.org/I145897649"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haolin Wang","raw_affiliation_strings":["Minzu University of China, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Minzu University of China, China","institution_ids":["https://openalex.org/I145897649"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100628752","display_name":"Wei Song","orcid":"https://orcid.org/0000-0002-2324-4302"},"institutions":[{"id":"https://openalex.org/I145897649","display_name":"Minzu University of China","ror":"https://ror.org/0044e2g62","country_code":"CN","type":"education","lineage":["https://openalex.org/I145897649"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Song","raw_affiliation_strings":["Minzu University of China, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Minzu University of China, China","institution_ids":["https://openalex.org/I145897649"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066464216","display_name":"Jianxin Xia","orcid":"https://orcid.org/0000-0002-6571-0957"},"institutions":[{"id":"https://openalex.org/I3125743391","display_name":"China University of Geosciences (Beijing)","ror":"https://ror.org/04q6c7p66","country_code":"CN","type":"education","lineage":["https://openalex.org/I3125743391"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianxin Xia","raw_affiliation_strings":["China University of Geosciences, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China University of Geosciences, China","institution_ids":["https://openalex.org/I3125743391"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059308309","display_name":"Tongmu Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tongmu Liu","raw_affiliation_strings":["South China Sea Marine Surve and Technology Center, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China Sea Marine Surve and Technology Center, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.879,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.77161185,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"278","last_page":"284"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10399","display_name":"Hydrocarbon exploration and reservoir analysis","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12282","display_name":"Mineral Processing and Grinding","score":0.9758999943733215,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7238897085189819},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7078999280929565},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6926196217536926},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6645175218582153},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5721836090087891},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.546185314655304},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5316705703735352},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.46069759130477905},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.326944500207901}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7238897085189819},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7078999280929565},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6926196217536926},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6645175218582153},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5721836090087891},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.546185314655304},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5316705703735352},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.46069759130477905},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.326944500207901},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3472634.3474302","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3472634.3474302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Turing Award Celebration Conference - China ( ACM TURC 2021)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/14","score":0.7799999713897705,"display_name":"Life below water"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W80841109","https://openalex.org/W639708223","https://openalex.org/W1861492603","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1975262195","https://openalex.org/W1982711079","https://openalex.org/W2076725774","https://openalex.org/W2128060444","https://openalex.org/W2136411942","https://openalex.org/W2291823763","https://openalex.org/W2746457594","https://openalex.org/W2763926640","https://openalex.org/W2945932878","https://openalex.org/W2951376261","https://openalex.org/W3018757597","https://openalex.org/W3044492391","https://openalex.org/W3045585186","https://openalex.org/W3113410735","https://openalex.org/W4248209014"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W4298130764","https://openalex.org/W2804364458","https://openalex.org/W2770593030","https://openalex.org/W2611989081","https://openalex.org/W3154990682","https://openalex.org/W2132641928","https://openalex.org/W4310225030","https://openalex.org/W2560201613","https://openalex.org/W1522196789"],"abstract_inverted_index":{"In":[0,93],"the":[1,31,37,62,67,74,97,132,141,150,163],"exploration":[2],"of":[3,33,43,69],"deep-sea":[4,44],"nodule":[5,17,34,104],"mineral":[6,18,45,105],"resources,":[7,19],"computer":[8],"vision-based":[9],"methods":[10,58],"have":[11],"been":[12],"widely":[13],"used":[14],"to":[15,60,83,95],"assess":[16],"such":[20,128],"as":[21,129],"abundance,":[22],"particle":[23],"size,":[24],"coverage":[25],"and":[26,54,91,131,157],"other":[27,55,123],"information.":[28],"Among":[29],"them,":[30],"separation":[32],"minerals":[35],"from":[36,64],"background":[38],"is":[39,118,147],"an":[40],"important":[41],"part":[42],"resource":[46],"assessment.":[47],"Threshold":[48],"segmentation,":[49,51],"clustering":[50],"multispectral":[52],"segmentation":[53,57,98,107],"traditional":[56],"need":[59],"rebuild":[61],"model":[63],"scratch":[65],"once":[66],"distribution":[68],"feature":[70],"space":[71],"changes.":[72],"However,":[73],"data-driven":[75],"deep":[76,102,125],"learning":[77,126],"method":[78,142,151],"provides":[79],"a":[80,100],"general":[81],"framework":[82],"solve":[84],"this":[85],"problem,":[86],"which":[87],"has":[88],"strong":[89],"generalization":[90],"robustness.":[92],"order":[94],"improve":[96],"performance,":[99],"novel":[101],"sea":[103],"images":[106],"algorithm":[108],"based":[109,143,152],"on":[110,121,144,153,162],"Mask":[111,145],"R-CNN":[112,146],"was":[113],"proposed.":[114],"The":[115,136],"comparative":[116],"analysis":[117],"also":[119],"processed":[120],"some":[122],"different":[124],"methods,":[127],"U-Net,":[130,154],"Generative":[133,159],"Adversarial":[134,160],"Network.":[135],"experimental":[137],"results":[138],"show":[139],"that,":[140],"better":[148],"than":[149],"improced":[155],"U-Net":[156],"Conditional":[158],"Networks":[161],"dataset.":[164]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}