{"id":"https://openalex.org/W3119769313","doi":"https://doi.org/10.1109/tim.2020.3047190","title":"Fabric Defect Segmentation Method Based on Deep Learning","display_name":"Fabric Defect Segmentation Method Based on Deep Learning","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3119769313","doi":"https://doi.org/10.1109/tim.2020.3047190","mag":"3119769313"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2020.3047190","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2020.3047190","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-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/A5050029439","display_name":"Yanqing Huang","orcid":"https://orcid.org/0000-0001-9289-4758"},"institutions":[{"id":"https://openalex.org/I27599042","display_name":"Xi'an Polytechnic University","ror":"https://ror.org/03442p831","country_code":"CN","type":"education","lineage":["https://openalex.org/I27599042"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanqing Huang","raw_affiliation_strings":["School of Electronics and Information, Xi\u2019an Polytechnic University, Xi\u2019an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Xi\u2019an Polytechnic University, Xi\u2019an, China","institution_ids":["https://openalex.org/I27599042"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103124195","display_name":"Junfeng Jing","orcid":null},"institutions":[{"id":"https://openalex.org/I27599042","display_name":"Xi'an Polytechnic University","ror":"https://ror.org/03442p831","country_code":"CN","type":"education","lineage":["https://openalex.org/I27599042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junfeng Jing","raw_affiliation_strings":["School of Electronics and Information, Xi\u2019an Polytechnic University, Xi\u2019an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Xi\u2019an Polytechnic University, Xi\u2019an, China","institution_ids":["https://openalex.org/I27599042"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100685729","display_name":"Zhen Wang","orcid":"https://orcid.org/0000-0002-3291-0768"},"institutions":[{"id":"https://openalex.org/I27599042","display_name":"Xi'an Polytechnic University","ror":"https://ror.org/03442p831","country_code":"CN","type":"education","lineage":["https://openalex.org/I27599042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Wang","raw_affiliation_strings":["School of Electronics and Information, Xi\u2019an Polytechnic University, Xi\u2019an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Xi\u2019an Polytechnic University, Xi\u2019an, China","institution_ids":["https://openalex.org/I27599042"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5050029439"],"corresponding_institution_ids":["https://openalex.org/I27599042"],"apc_list":null,"apc_paid":null,"fwci":13.3718,"has_fulltext":false,"cited_by_count":119,"citation_normalized_percentile":{"value":0.98911095,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"70","issue":null,"first_page":"1","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/T11595","display_name":"Textile materials and evaluations","score":0.9679999947547913,"subfield":{"id":"https://openalex.org/subfields/2507","display_name":"Polymers and Plastics"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9639000296592712,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/segmentation","display_name":"Segmentation","score":0.7798084020614624},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7596185207366943},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6985652446746826},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6922902464866638},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6329329013824463},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5229312181472778},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.51735919713974},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4656069576740265},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4616564214229584},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41867557168006897},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3575412631034851}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7798084020614624},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7596185207366943},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6985652446746826},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6922902464866638},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6329329013824463},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5229312181472778},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.51735919713974},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4656069576740265},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4616564214229584},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41867557168006897},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3575412631034851},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"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:10.1109/tim.2020.3047190","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2020.3047190","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6000000238418579,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W968791236","https://openalex.org/W984811894","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1687052005","https://openalex.org/W1849277567","https://openalex.org/W1901129140","https://openalex.org/W1964041634","https://openalex.org/W1965696824","https://openalex.org/W1981759979","https://openalex.org/W1993954744","https://openalex.org/W1995562189","https://openalex.org/W1999478155","https://openalex.org/W2003660891","https://openalex.org/W2034851609","https://openalex.org/W2034856412","https://openalex.org/W2048132605","https://openalex.org/W2053996691","https://openalex.org/W2054278237","https://openalex.org/W2088398118","https://openalex.org/W2097117768","https://openalex.org/W2100495367","https://openalex.org/W2102605133","https://openalex.org/W2102669124","https://openalex.org/W2108598243","https://openalex.org/W2110798204","https://openalex.org/W2112796928","https://openalex.org/W2120694107","https://openalex.org/W2125465482","https://openalex.org/W2133013766","https://openalex.org/W2138610981","https://openalex.org/W2145456339","https://openalex.org/W2152878251","https://openalex.org/W2163605009","https://openalex.org/W2169029660","https://openalex.org/W2170869852","https://openalex.org/W2194775991","https://openalex.org/W2395611524","https://openalex.org/W2538843088","https://openalex.org/W2589306531","https://openalex.org/W2615908833","https://openalex.org/W2618530766","https://openalex.org/W2621703534","https://openalex.org/W2800240267","https://openalex.org/W2883481603","https://openalex.org/W2923486253","https://openalex.org/W2937534293","https://openalex.org/W2963881378","https://openalex.org/W2964309882","https://openalex.org/W2971018391","https://openalex.org/W3104156061","https://openalex.org/W4255391993","https://openalex.org/W6625393997","https://openalex.org/W6637373629","https://openalex.org/W6676481782","https://openalex.org/W6685314622"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W4315434538","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Fabric":[0],"defect":[1,22,70,93,181],"detection":[2,195],"plays":[3],"an":[4,64],"essential":[5],"role":[6],"in":[7,16,56,230],"the":[8,17,36,40,44,81,86,101,108,126,135,138,142,145,151,156,167,191,222],"textile":[9,18],"production":[10],"process,":[11],"which":[12],"was":[13],"widely":[14],"applied":[15,149],"industry.":[19],"For":[20],"fabric":[21,127,217],"detection,":[23,39],"many":[24],"algorithms":[25],"have":[26],"been":[27],"proposed.":[28],"However,":[29],"lots":[30],"of":[31,38,43,76,85,104,110,137,144,169,193,199,232],"important":[32],"problems,":[33],"such":[34],"as":[35,134,150],"accuracy":[37,233],"computational":[41],"complexity":[42],"algorithm,":[45],"and":[46,72,106,123,188,214,234],"data":[47,87,128,212,218],"imbalance,":[48],"still":[49],"needed":[50],"to":[51,99,165,183],"be":[52],"addressed":[53],"for":[54,69,154],"application":[55],"industrial":[57],"production.":[58],"In":[59],"this":[60,77],"article,":[61],"we":[62],"propose":[63],"efficient":[65],"convolutional":[66],"neural":[67],"network":[68,116,147,162],"segmentation":[71,122,139,146,186],"detection.":[73],"The":[74,115,174,205],"design":[75],"framework":[78],"significantly":[79,225],"alleviates":[80],"manual":[82],"annotation":[83],"cost":[84],"set;":[88],"it":[89],"only":[90,177],"needs":[91],"few":[92],"samples":[94,98,182],"combined":[95],"with":[96,112,171,196],"standard":[97],"learn":[100],"potential":[102],"feature":[103],"defects":[105,111,170],"obtain":[107,166],"location":[109,168],"high":[113,172],"accuracy.":[114,173],"is":[117,132,148,163],"divided":[118],"into":[119],"two":[120],"parts:":[121],"decision.":[124],"First,":[125],"set":[129,213],"without":[130],"training":[131,155],"utilized":[133],"input":[136],"network.":[140,158],"Then,":[141],"output":[143],"raw":[152],"materials":[153],"decision":[157],"Finally,":[159],"a":[160,197,210],"well-trained":[161],"used":[164],"proposed":[175,223],"method":[176,224],"demands":[178],"almost":[179],"50":[180],"get":[184],"accurate":[185],"results":[187,207],"can":[189],"achieve":[190],"requirement":[192],"real-time":[194],"speed":[198],"25":[200],"frames":[201],"per":[202],"second":[203],"(FPS).":[204],"experimental":[206],"based":[208],"on":[209],"public":[211],"three":[215],"self-made":[216],"sets":[219],"show":[220],"that":[221],"outperforms":[226],"eight":[227],"state-of-the-art":[228],"methods":[229],"terms":[231],"robustness.":[235]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":28},{"year":2024,"cited_by_count":32},{"year":2023,"cited_by_count":30},{"year":2022,"cited_by_count":20},{"year":2021,"cited_by_count":7}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}
