{"id":"https://openalex.org/W2946313203","doi":"https://doi.org/10.1109/access.2019.2913620","title":"Fabric Defect Detection Using Activation Layer Embedded Convolutional Neural Network","display_name":"Fabric Defect Detection Using Activation Layer Embedded Convolutional Neural Network","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2946313203","doi":"https://doi.org/10.1109/access.2019.2913620","mag":"2946313203"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2913620","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2913620","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08701450.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08701450.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113528019","display_name":"Wenbin Ouyang","orcid":null},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenbin Ouyang","raw_affiliation_strings":["Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA","institution_ids":["https://openalex.org/I123534392"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043471749","display_name":"Bugao Xu","orcid":"https://orcid.org/0000-0001-9221-5110"},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bugao Xu","raw_affiliation_strings":["Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA"],"raw_orcid":"https://orcid.org/0000-0001-9221-5110","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA","institution_ids":["https://openalex.org/I123534392"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101541591","display_name":"Jue Hou","orcid":"https://orcid.org/0000-0003-2618-1391"},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jue Hou","raw_affiliation_strings":["Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA","institution_ids":["https://openalex.org/I123534392"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021355100","display_name":"Xiaohui Yuan","orcid":"https://orcid.org/0000-0001-6897-4563"},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaohui Yuan","raw_affiliation_strings":["Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA"],"raw_orcid":"https://orcid.org/0000-0001-6897-4563","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA","institution_ids":["https://openalex.org/I123534392"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I123534392"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":13.2283,"has_fulltext":true,"cited_by_count":137,"citation_normalized_percentile":{"value":0.98891208,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"7","issue":null,"first_page":"70130","last_page":"70140"},"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/T10638","display_name":"Optical measurement and interference techniques","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9890000224113464,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/loom","display_name":"LOOM","score":0.8312541246414185},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7747440338134766},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7555168867111206},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7350783348083496},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6133455634117126},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5514038801193237},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.50943523645401},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4890623986721039},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.48805201053619385},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4752483367919922},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4733419716358185}],"concepts":[{"id":"https://openalex.org/C2778584943","wikidata":"https://www.wikidata.org/wiki/Q6459541","display_name":"LOOM","level":2,"score":0.8312541246414185},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7747440338134766},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7555168867111206},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7350783348083496},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6133455634117126},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5514038801193237},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.50943523645401},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4890623986721039},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.48805201053619385},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4752483367919922},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4733419716358185}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2019.2913620","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2913620","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08701450.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:info:ark/67531/metadc1944145","is_oa":false,"landing_page_url":"https://digital.library.unt.edu/ark:/67531/metadc1944145/","pdf_url":null,"source":{"id":"https://openalex.org/S4306400792","display_name":"University of North Texas Digital Library (University of North Texas)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I123534392","host_organization_name":"University of North Texas","host_organization_lineage":["https://openalex.org/I123534392"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, 7, Institute of Electrical and Electronics Engineers, April 29, 2019, pp. 1-11","raw_type":"Article"},{"id":"pmh:oai:doaj.org/article:5795241543ce4df5aafbee19c92d8685","is_oa":true,"landing_page_url":"https://doaj.org/article/5795241543ce4df5aafbee19c92d8685","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 70130-70140 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2913620","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2913620","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08701450.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.5600000023841858,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2946313203.pdf","grobid_xml":"https://content.openalex.org/works/W2946313203.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W167016754","https://openalex.org/W1588043677","https://openalex.org/W1884984464","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1905829557","https://openalex.org/W1910657905","https://openalex.org/W1938976761","https://openalex.org/W1948751323","https://openalex.org/W1965365402","https://openalex.org/W1968969471","https://openalex.org/W2034851609","https://openalex.org/W2034856412","https://openalex.org/W2054279472","https://openalex.org/W2065781982","https://openalex.org/W2067173330","https://openalex.org/W2085525849","https://openalex.org/W2087801281","https://openalex.org/W2095729637","https://openalex.org/W2103220073","https://openalex.org/W2112076978","https://openalex.org/W2116090201","https://openalex.org/W2118978333","https://openalex.org/W2124592697","https://openalex.org/W2126413416","https://openalex.org/W2136367321","https://openalex.org/W2137664016","https://openalex.org/W2147880316","https://openalex.org/W2161236525","https://openalex.org/W2194775991","https://openalex.org/W2216949779","https://openalex.org/W2240965754","https://openalex.org/W2412782625","https://openalex.org/W2778854158","https://openalex.org/W2952793010","https://openalex.org/W4255391993","https://openalex.org/W6606837198","https://openalex.org/W6639824700","https://openalex.org/W6641763115","https://openalex.org/W6676769703","https://openalex.org/W6680247616","https://openalex.org/W6682082992","https://openalex.org/W6740164494"],"related_works":["https://openalex.org/W438422761","https://openalex.org/W569395298","https://openalex.org/W2367230280","https://openalex.org/W2387643047","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Loom":[0],"malfunctions":[1],"are":[2],"the":[3,45,96,106,110,114],"main":[4],"cause":[5],"of":[6,47,76,92,116],"faulty":[7],"fabric":[8,11,23,39,50],"production.":[9],"A":[10,62],"inspection":[12,41],"system":[13,19,42],"is":[14],"a":[15,31,70,119],"specialized":[16],"computer":[17],"vision":[18],"used":[20],"to":[21,69,73],"detect":[22],"defects":[24,94,117],"for":[25,36],"quality":[26],"assurance.":[27],"In":[28],"this":[29],"paper,":[30],"deep-learning":[32],"algorithm":[33],"was":[34,67],"developed":[35],"an":[37],"on-loom":[38],"defect":[40,54,77],"by":[43],"combining":[44],"techniques":[46],"image":[48],"pre-processing,":[49],"motif":[51],"determination,":[52],"candidate":[53],"map":[55],"generation,":[56],"and":[57,84,90,103,109],"convolutional":[58],"neural":[59],"networks":[60],"(CNNs).":[61],"novel":[63],"pairwise-potential":[64],"activation":[65],"layer":[66],"introduced":[68],"CNN,":[71],"leading":[72],"high":[74],"accuracy":[75,111],"segmentation":[78],"on":[79,112],"fabrics":[80],"with":[81],"intricate":[82],"features":[83],"imbalanced":[85],"dataset.":[86],"The":[87],"average":[88],"precision":[89],"recall":[91],"detecting":[93],"in":[95],"existing":[97],"images":[98],"reached,":[99],"respectively,":[100],"over":[101],"90%":[102],"80%":[104],"at":[105],"pixel":[107],"level":[108],"counting":[113],"number":[115],"from":[118],"publicly":[120],"available":[121],"dataset":[122],"exceeded":[123],"98%.":[124]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":28},{"year":2023,"cited_by_count":24},{"year":2022,"cited_by_count":28},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":16},{"year":2019,"cited_by_count":5}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
