{"id":"https://openalex.org/W4221055893","doi":"https://doi.org/10.1109/tnnls.2022.3152245","title":"Data Augmentation in Defect Detection of Sanitary Ceramics in Small and Non-i.i.d Datasets","display_name":"Data Augmentation in Defect Detection of Sanitary Ceramics in Small and Non-i.i.d Datasets","publication_year":2022,"publication_date":"2022-03-09","ids":{"openalex":"https://openalex.org/W4221055893","doi":"https://doi.org/10.1109/tnnls.2022.3152245","pmid":"https://pubmed.ncbi.nlm.nih.gov/35263260"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2022.3152245","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3152245","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xinyang Ren","orcid":"https://orcid.org/0000-0001-7221-2908"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyang Ren","raw_affiliation_strings":["Research Institute of Intelligent Control and Systems, Harbin Institution of Technology, Harbin, China"],"raw_orcid":"https://orcid.org/0000-0001-7221-2908","affiliations":[{"raw_affiliation_string":"Research Institute of Intelligent Control and Systems, Harbin Institution of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Weiyang Lin","orcid":"https://orcid.org/0000-0002-0493-1289"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiyang Lin","raw_affiliation_strings":["Research Institute of Intelligent Control and Systems, Harbin Institution of Technology, Harbin, China"],"raw_orcid":"https://orcid.org/0000-0002-0493-1289","affiliations":[{"raw_affiliation_string":"Research Institute of Intelligent Control and Systems, Harbin Institution of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xianqiang Yang","orcid":"https://orcid.org/0000-0002-0036-6921"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianqiang Yang","raw_affiliation_strings":["Research Institute of Intelligent Control and Systems, Harbin Institution of Technology, Harbin, China"],"raw_orcid":"https://orcid.org/0000-0002-0036-6921","affiliations":[{"raw_affiliation_string":"Research Institute of Intelligent Control and Systems, Harbin Institution of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xinghu Yu","orcid":"https://orcid.org/0000-0001-8181-6199"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinghu Yu","raw_affiliation_strings":["Ningbo Institute of Intelligent Equipment Technology Company, Ltd., Ningbo, China"],"raw_orcid":"https://orcid.org/0000-0001-8181-6199","affiliations":[{"raw_affiliation_string":"Ningbo Institute of Intelligent Equipment Technology Company, Ltd., Ningbo, China","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Huijun Gao","orcid":"https://orcid.org/0000-0001-5554-5452"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huijun Gao","raw_affiliation_strings":["Research Institute of Intelligent Control and Systems, Harbin Institution of Technology, Harbin, China"],"raw_orcid":"https://orcid.org/0000-0001-5554-5452","affiliations":[{"raw_affiliation_string":"Research Institute of Intelligent Control and Systems, Harbin Institution of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.4218,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.94627919,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"34","issue":"11","first_page":"8669","last_page":"8678"},"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":0.23520000278949738,"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":0.23520000278949738,"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/T12282","display_name":"Mineral Processing and Grinding","score":0.17260000109672546,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.0608999989926815,"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/sample","display_name":"Sample (material)","score":0.6252999901771545},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6007000207901001},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5346999764442444},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5343999862670898},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5156000256538391},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.5083000063896179},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.45559999346733093}],"concepts":[{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6252999901771545},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6007000207901001},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5555999875068665},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5346999764442444},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5343999862670898},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5156000256538391},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.5083000063896179},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49129998683929443},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.45559999346733093},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4244999885559082},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3668999969959259},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.34150001406669617},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31450000405311584},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C102094743","wikidata":"https://www.wikidata.org/wiki/Q133871","display_name":"Normal distribution","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2651999890804291},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C2989170903","wikidata":"https://www.wikidata.org/wiki/Q468402","display_name":"Ceramic tiles","level":3,"score":0.257099986076355},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2567000091075897}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2022.3152245","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3152245","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:35263260","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35263260","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4287915049","display_name":null,"funder_award_id":"2018YFB1308400","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2058668884","https://openalex.org/W2112796928","https://openalex.org/W2588463901","https://openalex.org/W2765811365","https://openalex.org/W2783561763","https://openalex.org/W2889205371","https://openalex.org/W2897772777","https://openalex.org/W2900595477","https://openalex.org/W2913926073","https://openalex.org/W2954860285","https://openalex.org/W2954996726","https://openalex.org/W2962770929","https://openalex.org/W2962843773","https://openalex.org/W2963446712","https://openalex.org/W2978896230","https://openalex.org/W2995696070","https://openalex.org/W2998508940","https://openalex.org/W3035574324","https://openalex.org/W3080551539","https://openalex.org/W3101538165","https://openalex.org/W3111635293","https://openalex.org/W3129082918","https://openalex.org/W3202544039","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6686164453","https://openalex.org/W6695314431","https://openalex.org/W6741832134","https://openalex.org/W6750558953","https://openalex.org/W6762744983","https://openalex.org/W6765779288","https://openalex.org/W6777046832","https://openalex.org/W6779093361","https://openalex.org/W6780041708","https://openalex.org/W6948377561"],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1,152,200],"study,":[2,153],"a":[3,55,158,195],"data-augmentation":[4],"method":[5,149],"is":[6,155,204],"proposed":[7,150],"to":[8,78,129,194,209],"narrow":[9],"the":[10,14,32,44,51,67,79,84,100,108,119,130,142,163,170,185,188,202],"significant":[11],"difference":[12],"between":[13,123,179],"distribution":[15,97,122,177],"of":[16,34,47,58,69,81,102,110,115,145,172,187],"training":[17,59,116,124,173,180],"and":[18,118,125,162,175,181,184],"test":[19,103,126,182],"sets":[20,127],"when":[21],"small":[22],"sample":[23,48],"sizes":[24],"are":[25,88],"concerned.":[26],"Two":[27],"major":[28],"obstacles":[29],"exist":[30],"in":[31,53,72,93,96,121,141,151,212],"process":[33],"defect":[35,74,86,143],"detection":[36,144],"on":[37,157,216],"sanitary":[38,146],"ceramics.":[39,147],"The":[40,113,148],"first":[41],"results":[42],"from":[43,206],"high":[45],"cost":[46],"collection,":[49],"namely,":[50],"difficulty":[52],"obtaining":[54],"large":[56],"number":[57,171],"images":[60,87,101,190],"required":[61],"by":[62],"deep-learning":[63],"algorithms,":[64],"which":[65,105,154],"limits":[66],"application":[68],"existing":[70,133],"algorithms":[71,136],"sanitary-ceramic":[73],"detection.":[75],"Second,":[76],"due":[77],"limitation":[80],"production":[82],"processes,":[83],"collected":[85],"often":[89],"marked,":[90],"thereby":[91],"resulting":[92],"great":[94],"differences":[95,120,178],"compared":[98],"with":[99],"sets,":[104,183],"further":[106],"affects":[107],"performance":[109],"detect-detection":[111],"algorithms.":[112],"lack":[114],"data":[117],"lead":[128],"fact":[131],"that":[132],"deep":[134],"learning-based":[135],"cannot":[137],"be":[138,192],"used":[139],"directly":[140],"based":[156],"generative":[159],"adversarial":[160],"network":[161],"Gaussian":[164],"mixture":[165],"model,":[166],"can":[167,191],"effectively":[168],"increase":[169],"samples":[174],"reduce":[176],"features":[186],"generated":[189],"controlled":[193],"certain":[196],"extent.":[197],"By":[198],"applying":[199],"method,":[201],"accuracy":[203],"improved":[205],"approximately":[207],"75%":[208],"nearly":[210],"90%":[211],"almost":[213],"all":[214],"experiments":[215],"different":[217],"classification":[218],"networks.":[219]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2022-04-03T00:00:00"}