{"id":"https://openalex.org/W3149873124","doi":"https://doi.org/10.1109/icbaie52039.2021.9390006","title":"A Method for Surface Defect Detection of Printed Circuit Board Based on Improved YOLOv4","display_name":"A Method for Surface Defect Detection of Printed Circuit Board Based on Improved YOLOv4","publication_year":2021,"publication_date":"2021-03-26","ids":{"openalex":"https://openalex.org/W3149873124","doi":"https://doi.org/10.1109/icbaie52039.2021.9390006","mag":"3149873124"},"language":"en","primary_location":{"id":"doi:10.1109/icbaie52039.2021.9390006","doi":"https://doi.org/10.1109/icbaie52039.2021.9390006","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icbaie52039.2021.9390006","pdf_url":null,"source":{"id":"https://openalex.org/S4363608182","display_name":"International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5077629531","display_name":"Haofei Xie","orcid":"https://orcid.org/0000-0001-9124-5035","observed_orcids":["https://orcid.org/0000-0001-9124-5035"]},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haofei Xie","raw_affiliation_strings":["School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100641084","display_name":"Yuan Li","orcid":"https://orcid.org/0000-0002-3503-293X","observed_orcids":["https://orcid.org/0000-0002-3503-293X"]},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Li","raw_affiliation_strings":["School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100749623","display_name":"Xingchen Li","orcid":"https://orcid.org/0000-0002-3691-9516","observed_orcids":["https://orcid.org/0000-0002-3691-9516"]},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingchen Li","raw_affiliation_strings":["School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100719323","display_name":"Li He","orcid":"https://orcid.org/0000-0003-4303-5281","observed_orcids":["https://orcid.org/0000-0003-4303-5281"]},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li He","raw_affiliation_strings":["School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I10535382"],"apc_list":null,"apc_paid":null,"fwci":6.9939,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.98116936,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":92,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"851","last_page":"857"},"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/printed-circuit-boards","display_name":"printed circuit boards","score":0.953000009059906},{"id":"https://openalex.org/keywords/surface-defect-detection","display_name":"surface defect detection","score":0.9929999709129333},{"id":"https://openalex.org/keywords/yolov4","display_name":"YOLOv4","score":0.8619999885559082},{"id":"https://openalex.org/keywords/small-object-detection","display_name":"small object detection","score":0.8009999990463257},{"id":"https://openalex.org/keywords/anchor-box-optimization","display_name":"anchor box optimization","score":0.640999972820282},{"id":"https://openalex.org/keywords/k-means-clustering","display_name":"K-means clustering","score":0.968999981880188},{"id":"https://openalex.org/keywords/data-augmentation","display_name":"data augmentation","score":0.8830000162124634},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":0.9610000252723694},{"id":"https://openalex.org/keywords/multi-scale-training","display_name":"multi-scale training","score":0.9409999847412109},{"id":"https://openalex.org/keywords/quality-inspection","display_name":"quality inspection","score":0.6460000276565552}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7053666114807129},{"id":"https://openalex.org/C120793396","wikidata":"https://www.wikidata.org/wiki/Q173350","display_name":"Printed circuit board","level":2,"score":0.6667498350143433},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6207517981529236},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6165918707847595},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5626919269561768},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5568041801452637},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5532115697860718},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5276502370834351},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.48087242245674133},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.42372629046440125},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33236628770828247},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17157065868377686},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icbaie52039.2021.9390006","doi":"https://doi.org/10.1109/icbaie52039.2021.9390006","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icbaie52039.2021.9390006","pdf_url":null,"source":{"id":"https://openalex.org/S4363608182","display_name":"International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2014705308","https://openalex.org/W2037227137","https://openalex.org/W2073459066","https://openalex.org/W2109255472","https://openalex.org/W2128272608","https://openalex.org/W2149933564","https://openalex.org/W2570343428","https://openalex.org/W2796347433","https://openalex.org/W2886157509","https://openalex.org/W2963857746","https://openalex.org/W3018757597","https://openalex.org/W3034713821","https://openalex.org/W3106250896"],"related_works":["https://openalex.org/W4362513054","https://openalex.org/W4200118103","https://openalex.org/W3134389753","https://openalex.org/W4409730934","https://openalex.org/W4413836326","https://openalex.org/W4413787911","https://openalex.org/W3137421590","https://openalex.org/W4392103192","https://openalex.org/W4405128803","https://openalex.org/W4361275271"],"abstract_inverted_index":{"The":[0,191],"quality":[1],"detection":[2,30,179,186,189,198],"of":[3,24,48,76,115,124,150,155,164],"printed":[4,49],"circuit":[5,50],"board":[6,51],"(PCB,":[7],"Printed":[8],"Circuit":[9],"Board)":[10],"is":[11],"a":[12,42],"very":[13],"important":[14],"task.":[15],"However,":[16],"due":[17],"to":[18,35,66,70,88,146,153],"the":[19,59,72,77,82,89,99,107,111,116,122,125,148,151,161,165,181,197,206],"diversity,":[20],"complexity":[21],"and":[22,92,120,136,142,159,175,188,196],"smallness":[23],"PCB":[25,117,214],"defects,":[26],"traditional":[27,174],"surface":[28,46,215],"defect":[29,130,216],"methods":[31,145],"are":[32],"still":[33],"difficult":[34],"deal":[36],"with.":[37],"Therefore,":[38],"this":[39],"paper":[40],"proposed":[41],"method":[43],"for":[44,127],"detecting":[45],"defects":[47,118],"based":[52],"on":[53,106],"improved":[54,121,160,182,185,207],"YOLOv4.":[55],"Firstly,":[56],"we":[57,97,133],"used":[58,98,134],"multistage":[60],"residual":[61],"hybrid":[62],"attention":[63,87],"module":[64],"(MRHAM)":[65],"improve":[67],"feature":[68,73,91],"learning":[69,141,177],"enhance":[71,147],"expression":[74],"ability":[75,163],"shallow":[78],"network,":[79],"so":[80],"that":[81,170,205],"receptive":[83],"field":[84],"pays":[85],"more":[86],"object":[90,129,178],"ignores":[93],"irrelevant":[94],"features;":[95],"Then,":[96],"K-means++":[100],"clustering":[101],"algorithm":[102],"perform":[103],"cluster":[104],"analysis":[105],"experimental":[108],"dataset,":[109,119],"determined":[110],"anchor":[112],"boxes":[113],"value":[114,193,212],"accuracy":[123,187],"model":[126,152,183],"small":[128],"positioning;":[131],"Finally,":[132],"online":[135],"offline":[137],"data":[138],"augmentation,":[139],"transfer":[140],"multi-scale":[143],"training":[144],"adaptability":[149],"images":[154],"different":[156],"input":[157],"scales":[158],"generalization":[162],"model.":[166],"Experimental":[167],"results":[168],"showed":[169,204],"compared":[171],"with":[172],"other":[173],"deep":[176],"models,":[180],"has":[184,209],"rate.":[190],"mAP":[192],"reaches":[194,200],"99.71%":[195],"rate":[199],"68":[201],"f.s-1.":[202],"It":[203],"YOLOv4":[208],"high":[210],"application":[211],"in":[213],"detection.":[217]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2021-04-13T00:00:00"}