{"id":"https://openalex.org/W4286582003","doi":"https://doi.org/10.1109/tim.2022.3193183","title":"PCBNet: A Lightweight Convolutional Neural Network for Defect Inspection in Surface Mount Technology","display_name":"PCBNet: A Lightweight Convolutional Neural Network for Defect Inspection in Surface Mount Technology","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4286582003","doi":"https://doi.org/10.1109/tim.2022.3193183"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2022.3193183","doi":"https://doi.org/10.1109/tim.2022.3193183","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2022.3193183","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,"listed_in":["anvur-scientific","cwts-core","dongbi-b","jufo-3","norway-1","poland-100","russia-white-list-1","vabb-shw"],"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"},"volume":null,"type":"article","is_traditional":true,"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/A5072941610","display_name":"Hongjin Wu","orcid":"https://orcid.org/0000-0002-5604-2512","observed_orcids":["https://orcid.org/0000-0002-5604-2512"]},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongjin Wu","raw_affiliation_strings":["School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-5604-2512","affiliations":[{"raw_affiliation_string":"School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032383311","display_name":"Ruoshan Lei","orcid":"https://orcid.org/0000-0002-2416-7652","observed_orcids":["https://orcid.org/0000-0002-2416-7652"]},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruoshan Lei","raw_affiliation_strings":["School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-2416-7652","affiliations":[{"raw_affiliation_string":"School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017168495","display_name":"Yibing Peng","orcid":"https://orcid.org/0000-0002-2464-4199","observed_orcids":["https://orcid.org/0000-0002-2464-4199"]},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yibing Peng","raw_affiliation_strings":["School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-2464-4199","affiliations":[{"raw_affiliation_string":"School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I47720641"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":null,"fwci":5.1067,"has_fulltext":false,"cited_by_count":66,"citation_normalized_percentile":{"value":0.96411936,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":100},"biblio":{"volume":"71","issue":null,"first_page":"1","last_page":"14"},"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":1.0,"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":1.0,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/surface-mount-technology","display_name":"surface mount technology","score":0.7929999828338623},{"id":"https://openalex.org/keywords/automated-optical-inspection","display_name":"automated optical inspection","score":0.8119999766349792},{"id":"https://openalex.org/keywords/lightweight-convolutional-neural-network","display_name":"lightweight convolutional neural network","score":0.8949999809265137},{"id":"https://openalex.org/keywords/component-localization","display_name":"component localization","score":0.718999981880188},{"id":"https://openalex.org/keywords/defect-classification","display_name":"defect classification","score":0.7269999980926514},{"id":"https://openalex.org/keywords/roi-segmentation","display_name":"region of interest segmentation","score":0.8669999837875366},{"id":"https://openalex.org/keywords/inference-latency","display_name":"inference latency","score":0.734000027179718},{"id":"https://openalex.org/keywords/oriented-object-detection","display_name":"oriented object detection","score":0.5139999985694885}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7512603998184204},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7334141731262207},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7023795247077942},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6436611413955688},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5968846082687378},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5831036567687988},{"id":"https://openalex.org/C2776584680","wikidata":"https://www.wikidata.org/wiki/Q191042","display_name":"Surface-mount technology","level":3,"score":0.5439100861549377},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5102477073669434},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4659275710582733},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4536556303501129},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4166390299797058},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40844911336898804},{"id":"https://openalex.org/C120793396","wikidata":"https://www.wikidata.org/wiki/Q173350","display_name":"Printed circuit board","level":2,"score":0.16184130311012268},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2022.3193183","doi":"https://doi.org/10.1109/tim.2022.3193183","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2022.3193183","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,"listed_in":["anvur-scientific","cwts-core","dongbi-b","jufo-3","norway-1","poland-100","russia-white-list-1","vabb-shw"],"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":[{"display_name":"Industry, innovation and infrastructure","score":0.5378999710083008,"id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G8492327114","display_name":null,"funder_award_id":"2018YFB1701702","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":67,"referenced_works":["https://openalex.org/W22745672","https://openalex.org/W639708223","https://openalex.org/W1677502123","https://openalex.org/W1976526581","https://openalex.org/W1985468640","https://openalex.org/W2027933820","https://openalex.org/W2030868862","https://openalex.org/W2044746995","https://openalex.org/W2054822316","https://openalex.org/W2056132907","https://openalex.org/W2095705004","https://openalex.org/W2101234009","https://openalex.org/W2170168007","https://openalex.org/W2193145675","https://openalex.org/W2295598076","https://openalex.org/W2612445135","https://openalex.org/W2612690371","https://openalex.org/W2728074186","https://openalex.org/W2746314669","https://openalex.org/W2752782242","https://openalex.org/W2768348081","https://openalex.org/W2779933875","https://openalex.org/W2790214586","https://openalex.org/W2796347433","https://openalex.org/W2883780447","https://openalex.org/W2886157509","https://openalex.org/W2898227265","https://openalex.org/W2902198254","https://openalex.org/W2911964244","https://openalex.org/W2914273126","https://openalex.org/W2932306369","https://openalex.org/W2944303778","https://openalex.org/W2944779197","https://openalex.org/W2944943292","https://openalex.org/W2946948417","https://openalex.org/W2949676527","https://openalex.org/W2950141105","https://openalex.org/W2962749812","https://openalex.org/W2962834855","https://openalex.org/W2963125010","https://openalex.org/W2963163009","https://openalex.org/W2979661171","https://openalex.org/W2984761674","https://openalex.org/W2994927529","https://openalex.org/W3008438817","https://openalex.org/W3010249414","https://openalex.org/W3016796920","https://openalex.org/W3035414587","https://openalex.org/W3039407588","https://openalex.org/W3088490277","https://openalex.org/W3089801272","https://openalex.org/W3106250896","https://openalex.org/W3136761610","https://openalex.org/W3137088683","https://openalex.org/W3145444543","https://openalex.org/W3174873843","https://openalex.org/W3184439416","https://openalex.org/W3201816303","https://openalex.org/W3203234901","https://openalex.org/W3204204809","https://openalex.org/W3206900642","https://openalex.org/W4225125774","https://openalex.org/W4394670654","https://openalex.org/W6695314431","https://openalex.org/W6750227808","https://openalex.org/W6793164127","https://openalex.org/W6798838024"],"related_works":["https://openalex.org/W4393171249","https://openalex.org/W4409365411","https://openalex.org/W4401650896","https://openalex.org/W7171525393","https://openalex.org/W4295036999","https://openalex.org/W7037137942","https://openalex.org/W6949503265","https://openalex.org/W4417278195","https://openalex.org/W2886157509","https://openalex.org/W4210245203"],"abstract_inverted_index":{"Prereflow":[0],"automatic":[1],"optical":[2],"inspection":[3,28],"(AOI)":[4],"has":[5],"been":[6],"widely":[7],"used":[8],"to":[9,55,150,183],"ensure":[10],"product":[11],"quality":[12],"in":[13,135,181],"surface":[14],"mount":[15],"technology":[16],"(SMT).":[17],"When":[18],"confronted":[19],"with":[20],"a":[21,39,83,120],"complex":[22],"industrial":[23],"environment,":[24],"traditional":[25,186],"hand-designed":[26],"visual":[27],"algorithms":[29],"may":[30],"lack":[31],"robustness":[32],"and":[33,51,59,93,139,169,189],"generalizability.":[34],"In":[35,62,108,171],"this":[36],"article,":[37],"PCBNet,":[38],"convolutional":[40],"neural":[41],"network":[42],"(CNN)":[43],"method":[44],"that":[45],"combines":[46],"data":[47,64],"preprocessing,":[48],"detection":[49,85,193],"network,":[50],"visualization,":[52],"is":[53,88],"proposed":[54,115],"localize":[56],"electronic":[57],"components":[58],"recognize":[60],"defects.":[61],"the":[63,98,102,105,111,114,155,159,163,176],"preprocessing":[65],"stage,":[66],"raw":[67],"images":[68],"are":[69,80],"segmented":[70],"into":[71],"several":[72],"regions":[73],"of":[74,90,113,137,179],"interest":[75],"(ROIs).":[76],"The":[77,126],"ROI":[78],"patches":[79],"inspected":[81],"by":[82],"CNN-based":[84],"system,":[86],"which":[87],"capable":[89],"classifying":[91],"defects":[92],"positioning":[94],"components.":[95],"After":[96],"inspection,":[97],"reporting":[99],"system":[100],"visualizes":[101],"results":[103],"via":[104],"human\u2013computer":[106],"interface.":[107],"comparative":[109],"studies,":[110],"effectiveness":[112],"PCBNet":[116,127,160,180],"was":[117],"validated":[118],"on":[119,154],"large-scale":[121],"PCB":[122],"component":[123],"defect":[124],"dataset.":[125],"backbone":[128],"outperforms":[129],"other":[130,151],"well-known":[131],"lightweight":[132],"CNN":[133],"backbones":[134],"terms":[136],"accuracy":[138],"latency":[140],"on$4\\times":[141],"$ARM":[142],"Cortex":[143],"A72":[144],"CPU":[145],"@":[146],"1.5":[147],"GHz.":[148],"Compared":[149],"learning-based":[152],"methods":[153],"small-scale":[156],"benchmark":[157],"dataset,":[158],"also":[161],"achieves":[162],"best":[164],"balance":[165],"between":[166],"inference":[167],"speed":[168],"accuracy.":[170],"addition,":[172],"extensive":[173],"experiments":[174],"demonstrate":[175],"superior":[177],"efficiency":[178],"comparison":[182],"some":[184],"famous":[185],"object":[187,192],"detectors":[188],"novel":[190],"oriented":[191],"algorithms.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":17},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2022-07-22T00:00:00"}