{"id":"https://openalex.org/W4323338462","doi":"https://doi.org/10.1109/access.2023.3252910","title":"Defect Detection Method Based on Knowledge Distillation","display_name":"Defect Detection Method Based on Knowledge Distillation","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4323338462","doi":"https://doi.org/10.1109/access.2023.3252910"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3252910","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3252910","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10058954.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/ielx7/6287639/6514899/10058954.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000454571","display_name":"Qunying Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153482","display_name":"Changzhou University","ror":"https://ror.org/04ymgwq66","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153482"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qunying Zhou","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China","institution_ids":["https://openalex.org/I4210153482"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100747741","display_name":"Hongyuan Wang","orcid":"https://orcid.org/0000-0003-1236-6141"},"institutions":[{"id":"https://openalex.org/I4210153482","display_name":"Changzhou University","ror":"https://ror.org/04ymgwq66","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153482"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyuan Wang","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China","institution_ids":["https://openalex.org/I4210153482"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105349176","display_name":"Ying Tang","orcid":"https://orcid.org/0009-0004-4751-4137"},"institutions":[{"id":"https://openalex.org/I4210153482","display_name":"Changzhou University","ror":"https://ror.org/04ymgwq66","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153482"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Tang","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China","institution_ids":["https://openalex.org/I4210153482"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011453689","display_name":"Yang Wang","orcid":"https://orcid.org/0000-0002-9107-6646"},"institutions":[{"id":"https://openalex.org/I4210153482","display_name":"Changzhou University","ror":"https://ror.org/04ymgwq66","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153482"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Wang","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China","institution_ids":["https://openalex.org/I4210153482"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5000454571"],"corresponding_institution_ids":["https://openalex.org/I4210153482"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":3.3275,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.92073351,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"11","issue":null,"first_page":"35866","last_page":"35873"},"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":0.9998000264167786,"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":0.9998000264167786,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9824000000953674,"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/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9786999821662903,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/distillation","display_name":"Distillation","score":0.7241406440734863},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7060481309890747},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6732365489006042},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6370428800582886},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6309069991111755},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.528076171875},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4476008415222168},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4272216856479645},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3929693102836609},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.09174007177352905},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.09047558903694153}],"concepts":[{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.7241406440734863},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7060481309890747},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6732365489006042},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6370428800582886},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6309069991111755},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.528076171875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4476008415222168},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4272216856479645},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3929693102836609},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.09174007177352905},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.09047558903694153},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3252910","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3252910","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10058954.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:oai:doaj.org/article:0be4ef2505014b32bc65dce52d12e5da","is_oa":true,"landing_page_url":"https://doaj.org/article/0be4ef2505014b32bc65dce52d12e5da","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 35866-35873 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3252910","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3252910","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10058954.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":[{"id":"https://metadata.un.org/sdg/4","score":0.6399999856948853,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4323338462.pdf","grobid_xml":"https://content.openalex.org/works/W4323338462.grobid-xml"},"referenced_works_count":57,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1821462560","https://openalex.org/W2108598243","https://openalex.org/W2288122362","https://openalex.org/W2294370754","https://openalex.org/W2412510955","https://openalex.org/W2508457857","https://openalex.org/W2531327146","https://openalex.org/W2565639579","https://openalex.org/W2599354622","https://openalex.org/W2739879705","https://openalex.org/W2752782242","https://openalex.org/W2784032999","https://openalex.org/W2805484002","https://openalex.org/W2809705434","https://openalex.org/W2907868778","https://openalex.org/W2944303778","https://openalex.org/W2953868242","https://openalex.org/W2955058313","https://openalex.org/W2962992847","https://openalex.org/W2963215781","https://openalex.org/W2963733773","https://openalex.org/W2982781781","https://openalex.org/W2994615081","https://openalex.org/W2995607862","https://openalex.org/W2996077526","https://openalex.org/W2997006708","https://openalex.org/W2999250053","https://openalex.org/W3023868590","https://openalex.org/W3034552520","https://openalex.org/W3094625083","https://openalex.org/W3118895125","https://openalex.org/W3155674883","https://openalex.org/W3157243312","https://openalex.org/W3169077988","https://openalex.org/W3170224286","https://openalex.org/W3192277055","https://openalex.org/W3201057920","https://openalex.org/W3209662019","https://openalex.org/W4206217610","https://openalex.org/W4207017358","https://openalex.org/W4226022956","https://openalex.org/W4245502741","https://openalex.org/W4281489207","https://openalex.org/W4287869122","https://openalex.org/W4289752563","https://openalex.org/W4312480718","https://openalex.org/W4312929415","https://openalex.org/W6637373629","https://openalex.org/W6682137061","https://openalex.org/W6715189028","https://openalex.org/W6728622933","https://openalex.org/W6752554887","https://openalex.org/W6769906912","https://openalex.org/W6771530079","https://openalex.org/W6777869702","https://openalex.org/W6803508786"],"related_works":["https://openalex.org/W3026162553","https://openalex.org/W2105642232","https://openalex.org/W3207332793","https://openalex.org/W3197833032","https://openalex.org/W4386081464","https://openalex.org/W2768175398","https://openalex.org/W2499612753","https://openalex.org/W3113278055","https://openalex.org/W2344382886","https://openalex.org/W19111321"],"abstract_inverted_index":{"Aiming":[0],"at":[1,122],"the":[2,44,48,52,61,65,68,75,80,87,92,103,109,113,117,123,128,133,137,141,146,153,168,171],"problem":[3],"that":[4,152],"traditional":[5],"surface":[6],"detection":[7,26,56,93,142],"is":[8,28],"easily":[9],"affected":[10],"by":[11],"complex":[12],"industrial":[13],"environments":[14],"and":[15,79,101,112,145,161],"cannot":[16],"extract":[17],"effective":[18],"features,":[19],"a":[20,31],"deep":[21],"learning-based":[22],"knowledge":[23,39,130],"distillation":[24,131],"anomaly":[25,147],"model":[27,139],"proposed.":[29],"Firstly,":[30],"pre-trained":[32],"teacher":[33,88,110],"network":[34,46,70,111],"was":[35,57,120],"used":[36],"to":[37,43,86,159],"transfer":[38],"of":[40,67,95,98,136,170],"normal":[41],"samples":[42],"student":[45,114],"in":[47,64,105,155],"training":[49],"phase.":[50],"In":[51],"testing":[53],"phase,":[54],"defect":[55,118],"achieved":[58],"based":[59],"on":[60,140,143,149,163],"feature":[62,81],"differences":[63],"output":[66,106],"teacher-student":[69],"for":[71],"abnormal":[72],"samples.":[73],"Secondly,":[74],"attention":[76],"mechanism":[77],"module":[78,83],"fusion":[82],"were":[84],"added":[85],"network,":[89],"which":[90,166],"enhanced":[91],"ability":[94],"various":[96],"defects":[97],"different":[99],"sizes":[100],"increased":[102],"difference":[104],"features":[107],"between":[108],"network.":[115],"Finally,":[116],"image":[119],"located":[121],"pixel":[124],"level.":[125],"Compared":[126],"with":[127],"advanced":[129],"methods,":[132],"experimental":[134],"results":[135],"proposed":[138],"MVTecAD":[144,150],"localization":[148],"showed":[151],"method":[154],"this":[156],"paper":[157],"improved":[158],"90.3%":[160],"90.1%":[162],"AUROC":[164],"respectively,":[165],"verified":[167],"effectiveness":[169],"method.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
