{"id":"https://openalex.org/W4393171249","doi":"https://doi.org/10.1109/tim.2024.3381700","title":"Reliable and Lightweight Adaptive Convolution Network for PCB Surface Defect Detection","display_name":"Reliable and Lightweight Adaptive Convolution Network for PCB Surface Defect Detection","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4393171249","doi":"https://doi.org/10.1109/tim.2024.3381700"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2024.3381700","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2024.3381700","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/A5100381351","display_name":"Lei Lei","orcid":"https://orcid.org/0000-0002-3630-3359"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Lei Lei","raw_affiliation_strings":["Department of Systems Engineering, City University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"Department of Systems Engineering, City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090216201","display_name":"Han\u2010Xiong Li","orcid":"https://orcid.org/0000-0002-0707-5940"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Han-Xiong Li","raw_affiliation_strings":["Department of Systems Engineering, City University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"Department of Systems Engineering, City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007248533","display_name":"Haidong Yang","orcid":"https://orcid.org/0000-0002-6734-249X"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hai-Dong Yang","raw_affiliation_strings":["Guangdong Engineering Research Center for Green Manufacturing and Energy Efficiency Optimization, Guangdong University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Engineering Research Center for Green Manufacturing and Energy Efficiency Optimization, Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100381351"],"corresponding_institution_ids":["https://openalex.org/I168719708"],"apc_list":null,"apc_paid":null,"fwci":4.8893,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.94991291,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"73","issue":null,"first_page":"1","last_page":"8"},"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":0.9997000098228455,"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":0.9997000098228455,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9700000286102295,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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":0.9448000192642212,"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/convolution","display_name":"Convolution (computer science)","score":0.6392716765403748},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4856513738632202},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.4568210244178772},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.420535683631897},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.3511200547218323},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.24792662262916565},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2232992947101593},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.182882159948349},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15678095817565918}],"concepts":[{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6392716765403748},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4856513738632202},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.4568210244178772},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.420535683631897},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.3511200547218323},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.24792662262916565},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2232992947101593},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.182882159948349},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15678095817565918},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2024.3381700","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2024.3381700","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":[{"id":"https://metadata.un.org/sdg/7","score":0.44999998807907104,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2914237854","https://openalex.org/W2922005503","https://openalex.org/W2963163009","https://openalex.org/W2971018118","https://openalex.org/W3028888497","https://openalex.org/W3033847194","https://openalex.org/W3035006120","https://openalex.org/W3048213795","https://openalex.org/W3108566774","https://openalex.org/W3113968199","https://openalex.org/W3164289800","https://openalex.org/W3172914438","https://openalex.org/W3197644417","https://openalex.org/W4214576162","https://openalex.org/W4285131332","https://openalex.org/W4285219337","https://openalex.org/W4286582003","https://openalex.org/W4292972665","https://openalex.org/W4312687216","https://openalex.org/W4313064927","https://openalex.org/W4313887241","https://openalex.org/W4361759853","https://openalex.org/W4367043778","https://openalex.org/W4377691132","https://openalex.org/W4378965951","https://openalex.org/W4381415877","https://openalex.org/W4386404700","https://openalex.org/W4389215987","https://openalex.org/W4390659789"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W4246450666","https://openalex.org/W4388998267","https://openalex.org/W2898370298","https://openalex.org/W2137437058","https://openalex.org/W4390401159","https://openalex.org/W2744391499","https://openalex.org/W4230250635","https://openalex.org/W3041790586","https://openalex.org/W2018879842"],"abstract_inverted_index":{"Surface":[0],"defect":[1,30,104],"detection":[2,124],"is":[3,42,51],"very":[4],"important":[5],"for":[6,27,38],"the":[7,45,72,95,99,110,115],"printed":[8],"circuit":[9],"board":[10],"(PCB)":[11],"to":[12,78,108],"ensure":[13],"their":[14],"quality":[15],"requirements.":[16],"This":[17],"paper":[18],"proposes":[19],"a":[20],"reliable":[21],"and":[22,44,64,87,91,123],"lightweight":[23,56],"adaptive":[24,57],"convolution":[25,58,62],"network":[26],"PCB":[28,40,49],"surface":[29],"detection.":[31],"First,":[32],"an":[33],"automated":[34],"optical":[35],"inspection":[36],"(AOI)":[37],"collecting":[39],"defects":[41,50],"introduced,":[43],"formation":[46],"mechanism":[47],"of":[48,98],"systematically":[52],"analyzed.":[53],"After":[54],"that,":[55],"strategically":[59],"aggregates":[60],"multiple":[61],"kernels":[63],"simplifies":[65],"model":[66],"complexity":[67],"through":[68],"tensor":[69],"decomposition.":[70],"Furthermore,":[71],"confidence":[73,88],"gate":[74],"learning":[75,86],"strategy":[76],"aims":[77],"cope":[79],"with":[80],"dataset":[81],"noise":[82],"by":[83],"combining":[84],"collaborative":[85],"evaluation.":[89],"Complexity":[90],"convergence":[92],"analyses":[93],"support":[94],"theoretical":[96],"basis":[97],"method.":[100],"Finally,":[101],"three":[102],"industrial":[103],"datasets":[105],"are":[106],"used":[107],"evaluate":[109],"effectiveness.":[111],"The":[112],"results":[113],"show":[114],"methodology":[116],"has":[117],"powerful":[118],"feature":[119],"representation,":[120],"visual":[121],"interpretability,":[122],"robustness.":[125]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
