{"id":"https://openalex.org/W3137088683","doi":"https://doi.org/10.1109/tim.2021.3067221","title":"DefectNet: Toward Fast and Effective Defect Detection","display_name":"DefectNet: Toward Fast and Effective Defect Detection","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3137088683","doi":"https://doi.org/10.1109/tim.2021.3067221","mag":"3137088683"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2021.3067221","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2021.3067221","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/A5100376064","display_name":"Li Feng","orcid":"https://orcid.org/0000-0001-6128-3663"},"institutions":[{"id":"https://openalex.org/I181326427","display_name":"Donghua University","ror":"https://ror.org/035psfh38","country_code":"CN","type":"education","lineage":["https://openalex.org/I181326427"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Li","raw_affiliation_strings":["School of Computer Science and Technology, Donghua University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-6128-3663","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Donghua University, Shanghai, China","institution_ids":["https://openalex.org/I181326427"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090779515","display_name":"Feng Li","orcid":"https://orcid.org/0000-0002-7337-5861"},"institutions":[{"id":"https://openalex.org/I181326427","display_name":"Donghua University","ror":"https://ror.org/035psfh38","country_code":"CN","type":"education","lineage":["https://openalex.org/I181326427"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Li","raw_affiliation_strings":["School of Computer Science and Technology, Donghua University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-7337-5861","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Donghua University, Shanghai, China","institution_ids":["https://openalex.org/I181326427"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020488911","display_name":"Qinggang Xi","orcid":"https://orcid.org/0000-0001-9668-8388"},"institutions":[{"id":"https://openalex.org/I181326427","display_name":"Donghua University","ror":"https://ror.org/035psfh38","country_code":"CN","type":"education","lineage":["https://openalex.org/I181326427"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"QingGang Xi","raw_affiliation_strings":["School of Computer Science and Technology, Donghua University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-9668-8388","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Donghua University, Shanghai, China","institution_ids":["https://openalex.org/I181326427"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I181326427"],"apc_list":null,"apc_paid":null,"fwci":5.5926,"has_fulltext":false,"cited_by_count":47,"citation_normalized_percentile":{"value":0.96139022,"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":"9"},"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9739999771118164,"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/T14319","display_name":"Currency Recognition and Detection","score":0.9721999764442444,"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/object-detection","display_name":"Object detection","score":0.7957613468170166},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.762319028377533},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6443119049072266},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5847811698913574},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5825033187866211},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4840928316116333},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4565872251987457},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43002089858055115}],"concepts":[{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7957613468170166},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.762319028377533},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6443119049072266},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5847811698913574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5825033187866211},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4840928316116333},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4565872251987457},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43002089858055115},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2021.3067221","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2021.3067221","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1686810756","https://openalex.org/W1861492603","https://openalex.org/W2031489346","https://openalex.org/W2102605133","https://openalex.org/W2194775991","https://openalex.org/W2536297875","https://openalex.org/W2565639579","https://openalex.org/W2795647708","https://openalex.org/W2899280016","https://openalex.org/W2944303778","https://openalex.org/W2946948417","https://openalex.org/W2950141105","https://openalex.org/W2955425717","https://openalex.org/W2963037989","https://openalex.org/W2963351448","https://openalex.org/W2964241181","https://openalex.org/W2998228095","https://openalex.org/W3035473155","https://openalex.org/W3106250896","https://openalex.org/W4288325606","https://openalex.org/W6637373629","https://openalex.org/W6762718338","https://openalex.org/W6764322716","https://openalex.org/W6772899810","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W3038672259","https://openalex.org/W3135818718","https://openalex.org/W2767651786","https://openalex.org/W2095705906","https://openalex.org/W2912288872","https://openalex.org/W564581980","https://openalex.org/W2970686063","https://openalex.org/W4311401716","https://openalex.org/W2801801420","https://openalex.org/W3090693157"],"abstract_inverted_index":{"The":[0],"existing":[1],"object":[2,56,88,97,175],"detection":[3,17,25,30,37,44,52,89,98,116,149,168,176],"algorithms":[4,31],"based":[5,100,178],"on":[6,101,179],"the":[7,16,36,43,51,92,123,143,148,153,167,173],"convolutional":[8],"neural":[9],"network":[10,117,136,150,177],"(CNN)":[11],"are":[12,83],"always":[13],"devoted":[14],"to":[15,35,121,137,151],"of":[18,38,45,53,64,94,125,172],"natural":[19,55],"objects":[20],"and":[21,72,145,157,170],"have":[22,32],"achieved":[23],"admirable":[24],"effects.":[26],"At":[27],"present,":[28],"these":[29],"been":[33],"applied":[34],"defect":[39,46,115,126],"data.":[40,57],"In":[41,110],"fact,":[42],"data":[47],"is":[48,119,183],"different":[49,76,85],"from":[50,86],"general":[54,87,96,174],"First,":[58],"it":[59,163],"has":[60],"a":[61,95,113,131],"large":[62],"number":[63],"images":[65],"without":[66],"annotations":[67],"(that":[68],"is,":[69],"normal":[70],"images),":[71],"they":[73],"each":[74],"contain":[75],"background":[77],"information.":[78],"Second,":[79],"its":[80],"processing":[81],"principles":[82],"fundamentally":[84],"problems.":[90],"Therefore,":[91],"application":[93],"algorithm":[99],"CNN":[102],"may":[103],"not":[104],"be":[105],"perfect":[106],"in":[107],"this":[108,111],"problem.":[109],"article,":[112],"novel":[114],"(DefectNet)":[118],"proposed":[120],"solve":[122],"problem":[124],"detection.":[127],"It":[128],"first":[129],"uses":[130,147],"shared":[132],"weight":[133],"binary":[134],"classification":[135],"determine":[138],"whether":[139],"an":[140],"image":[141],"contains":[142],"targets":[144],"then":[146],"detect":[152],"targets.":[154],"Theoretical":[155],"deduction":[156],"experimental":[158],"results":[159],"fully":[160],"confirm":[161],"that":[162],"can":[164],"effectively":[165],"improve":[166],"speed":[169],"effect":[171],"CNN.":[180],"(Our":[181],"code":[182],"available":[184],"at":[185],"<uri":[186],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[187],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">https://github.com/li-phone/DefectNet.git</uri>":[188],")":[189]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":16}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}