{"id":"https://openalex.org/W4321380904","doi":"https://doi.org/10.1109/tim.2023.3246499","title":"Ferrite Beads Surface Defect Detection Based on Spatial Attention Under Weakly Supervised Learning","display_name":"Ferrite Beads Surface Defect Detection Based on Spatial Attention Under Weakly Supervised Learning","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4321380904","doi":"https://doi.org/10.1109/tim.2023.3246499"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2023.3246499","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3246499","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/A5046983648","display_name":"Yiming Li","orcid":"https://orcid.org/0000-0002-3336-4463"},"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":true,"raw_author_name":"Yiming Li","raw_affiliation_strings":["School of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026068874","display_name":"Xiaojun Wu","orcid":"https://orcid.org/0000-0003-4988-5420"},"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":"Xiaojun Wu","raw_affiliation_strings":["School of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100432616","display_name":"Peng Li","orcid":"https://orcid.org/0000-0001-5438-0756"},"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":"Peng Li","raw_affiliation_strings":["School of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100707660","display_name":"Yunhui Liu","orcid":"https://orcid.org/0000-0002-3625-6679"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunhui Liu","raw_affiliation_strings":["Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, SAR, China"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, SAR, China","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5046983648"],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":4.1489,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.93907488,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"72","issue":null,"first_page":"1","last_page":"12"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9965999722480774,"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/T13049","display_name":"Surface Roughness and Optical Measurements","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7330063581466675},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6972531080245972},{"id":"https://openalex.org/keywords/visual-inspection","display_name":"Visual inspection","score":0.5102169513702393},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5004692077636719},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.499666690826416},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48385512828826904},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4420248568058014},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3603435158729553}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7330063581466675},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6972531080245972},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.5102169513702393},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5004692077636719},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.499666690826416},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48385512828826904},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4420248568058014},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3603435158729553}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2023.3246499","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3246499","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":0.44999998807907104,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G1907891493","display_name":null,"funder_award_id":"52175459","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8378505745","display_name":null,"funder_award_id":"62073099","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2295107390","https://openalex.org/W2306289963","https://openalex.org/W2418691539","https://openalex.org/W2800240267","https://openalex.org/W2891336752","https://openalex.org/W2897689496","https://openalex.org/W2912729634","https://openalex.org/W2914570111","https://openalex.org/W2923486253","https://openalex.org/W2944303778","https://openalex.org/W2963064675","https://openalex.org/W2970822152","https://openalex.org/W2982770724","https://openalex.org/W2994615081","https://openalex.org/W2998008435","https://openalex.org/W3008277736","https://openalex.org/W3010618895","https://openalex.org/W3034314048","https://openalex.org/W3046457381","https://openalex.org/W3082184285","https://openalex.org/W3093125336","https://openalex.org/W3104156061","https://openalex.org/W3122404540","https://openalex.org/W3129082918","https://openalex.org/W3138516171","https://openalex.org/W3153381206","https://openalex.org/W3159712850","https://openalex.org/W3161141656","https://openalex.org/W3176760243","https://openalex.org/W3199265447","https://openalex.org/W3203176001","https://openalex.org/W4206551889","https://openalex.org/W4226320329","https://openalex.org/W4297665946","https://openalex.org/W4385245566","https://openalex.org/W6637373629","https://openalex.org/W6775845032"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2055243143","https://openalex.org/W2781569684","https://openalex.org/W2611989081","https://openalex.org/W2478098815","https://openalex.org/W4230611425","https://openalex.org/W2731899572","https://openalex.org/W4290692565","https://openalex.org/W2371486462","https://openalex.org/W4380075502"],"abstract_inverted_index":{"Ferrite":[0],"beads\u2019":[1],"automatic":[2],"surface":[3],"inspection":[4,21,46,218],"is":[5,107,230],"an":[6,148],"important":[7],"means":[8],"to":[9,61,97,109,125,138,152,163,197],"improve":[10,98,110,153],"the":[11,18,41,80,99,111,116,127,135,154,176,180,194,216,223,227,248],"quality":[12,237],"and":[13,67,119,131,168,209,225,245,257],"ensure":[14],"proper":[15],"operation.":[16],"Although":[17],"deep-learning-based":[19,45],"defect":[20,58,129,173,187,252],"methods":[22,27],"reveal":[23],"powerful":[24],"performance,":[25],"these":[26],"often":[28],"require":[29],"a":[30,54,88,103,165,199],"large":[31],"amount":[32],"of":[33,44,70,84,156,204,251,254],"expensive":[34],"annotation":[35],"data":[36],"for":[37,172,235],"training,":[38],"which":[39,115,211],"limits":[40],"practical":[42],"application":[43],"methods.":[47],"To":[48,77,220],"solve":[49],"this":[50],"problem,":[51],"we":[52,86,145],"propose":[53],"weakly":[55],"supervised":[56],"learning":[57],"detection":[59,112],"algorithm":[60],"achieve":[62,198,247],"both":[63],"high":[64],"accuracy":[65],"identification":[66],"effective":[68,169],"localization":[69,157],"defects":[71],"while":[72],"using":[73],"only":[74,190],"image-level":[75,191],"labels.":[76],"better":[78],"use":[79],"texture":[81],"location":[82],"information":[83],"defects,":[85],"present":[87],"spatial":[89],"association":[90],"module":[91],"(SAM)":[92],"based":[93],"on":[94,232],"shallow":[95],"features":[96],"network":[100],"performance.":[101],"Then":[102],"training":[104,195],"enhancement":[105],"method":[106,151,182,229],"proposed":[108,181,228],"ability,":[113],"in":[114,134,140,193,215],"guide":[117],"crop":[118],"object":[120],"ignore":[121],"algorithms":[122],"are":[123],"used":[124,214],"extract":[126],"main":[128],"area":[130,133],"background":[132],"image,":[136],"respectively,":[137],"assist":[139],"generating":[141],"optimal":[142,149],"decisions.":[143],"Finally,":[144],"put":[146],"forward":[147],"inference":[150],"completeness":[155],"without":[158],"sacrificing":[159],"accuracy,":[160],"so":[161],"as":[162],"provide":[164],"more":[166],"reasonable":[167],"visual":[170],"explanation":[171],"recognition.":[174],"On":[175],"ferrite":[177],"bead":[178],"dataset,":[179],"uses":[183],"less":[184],"than":[185],"200":[186],"samples":[188],"with":[189,206],"labels":[192],"process":[196],"classification":[200,253],"average":[201],"precision":[202],"(AP)":[203],"97.1%":[205],"good":[207],"stability":[208],"reliability,":[210],"has":[212],"been":[213],"ferrite-bead":[217],"machine.":[219],"further":[221],"verify":[222],"superiority":[224],"generalization,":[226],"evaluated":[231],"several":[233],"datasets":[234],"industrial":[236],"inspection:":[238],"Deutsche":[239],"Arbeitsgemeinschaft":[240],"fuer":[241],"Mustererkennung":[242],"(DAGM),":[243],"KolektorSDD,":[244],"KolektorSDD2":[246],"best":[249],"AP":[250],"100%,":[255,256],"99.9%,":[258],"respectively.":[259]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
