{"id":"https://openalex.org/W7134912317","doi":"https://doi.org/10.1109/access.2026.3672639","title":"Research on Improved Model for PCB Image Defect Detection Based on YOLOv11","display_name":"Research on Improved Model for PCB Image Defect Detection Based on YOLOv11","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7134912317","doi":"https://doi.org/10.1109/access.2026.3672639"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3672639","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3672639","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3672639","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100335043","display_name":"Jun Wang","orcid":"https://orcid.org/0000-0002-5901-9019"},"institutions":[{"id":"https://openalex.org/I4510145","display_name":"Jiangxi University of Science and Technology","ror":"https://ror.org/03q0t9252","country_code":"CN","type":"education","lineage":["https://openalex.org/I4510145"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jun Wang","raw_affiliation_strings":["School of Electrical and Mechanical Engineering, Jiangxi University of Science and Technology, Ganzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Mechanical Engineering, Jiangxi University of Science and Technology, Ganzhou, China","institution_ids":["https://openalex.org/I4510145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128733077","display_name":"Youbin Gong","orcid":null},"institutions":[{"id":"https://openalex.org/I4510145","display_name":"Jiangxi University of Science and Technology","ror":"https://ror.org/03q0t9252","country_code":"CN","type":"education","lineage":["https://openalex.org/I4510145"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Youbin Gong","raw_affiliation_strings":["School of Electrical and Mechanical Engineering, Jiangxi University of Science and Technology, Ganzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Mechanical Engineering, Jiangxi University of Science and Technology, Ganzhou, China","institution_ids":["https://openalex.org/I4510145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5128777975","display_name":"Jiawei Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4510145","display_name":"Jiangxi University of Science and Technology","ror":"https://ror.org/03q0t9252","country_code":"CN","type":"education","lineage":["https://openalex.org/I4510145"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Wang","raw_affiliation_strings":["School of Electrical and Mechanical Engineering, Jiangxi University of Science and Technology, Ganzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Mechanical Engineering, Jiangxi University of Science and Technology, Ganzhou, China","institution_ids":["https://openalex.org/I4510145"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100335043"],"corresponding_institution_ids":["https://openalex.org/I4510145"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.77865537,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"40788","last_page":"40804"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6935999989509583,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6935999989509583,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.25279998779296875,"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/T13918","display_name":"Advanced Data and IoT Technologies","score":0.007499999832361937,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/feature-extraction","display_name":"Feature extraction","score":0.6583999991416931},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.5776000022888184},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.569100022315979},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.565500020980835},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5595999956130981},{"id":"https://openalex.org/keywords/offset","display_name":"Offset (computer science)","score":0.5361999869346619},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5008999705314636},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.48910000920295715},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4147000014781952}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6777999997138977},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6583999991416931},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.635200023651123},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.5776000022888184},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.569100022315979},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.565500020980835},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5595999956130981},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.5361999869346619},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5008999705314636},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.48910000920295715},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4666000008583069},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4147000014781952},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4099000096321106},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3463999927043915},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.30880001187324524},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3061999976634979},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.2919999957084656},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.28610000014305115},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2777000069618225},{"id":"https://openalex.org/C122048520","wikidata":"https://www.wikidata.org/wiki/Q2913954","display_name":"Percentile","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.25859999656677246},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25459998846054077}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3672639","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3672639","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:33aa29f7da4c4280b3335ef66a332e63","is_oa":true,"landing_page_url":"https://doaj.org/article/33aa29f7da4c4280b3335ef66a332e63","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 40788-40804 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3672639","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3672639","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,97],"order":[1],"to":[2,38,76,81,105],"address":[3],"the":[4,56,73,78,83,108,111,118,125,150,156,165,189],"challenge":[5],"of":[6,60,86,114,129,191],"detecting":[7],"small":[8,130,195],"surface":[9,144],"defects":[10,62,87],"on":[11],"PCB":[12,143],"electrical":[13],"and":[14,31,51,54,93,117,164,176,184,199],"electronic":[15],"components,":[16],"this":[17,192],"study":[18],"proposes":[19],"a":[20,40,99],"model":[21,193],"called":[22],"SOF-YOLO.":[23],"SOF-YOLO":[24,138],"introduces":[25],"Spatial":[26],"Pyramid":[27],"Dilated":[28],"Convolution":[29],"(SPDConv)":[30],"OmniKernel":[32],"Cross":[33],"Stage":[34],"Partial":[35],"(OK-CSP)":[36],"modules":[37],"construct":[39],"P2":[41],"layer":[42],"feature":[43,57,197],"extraction":[44],"path,":[45],"which":[46],"integrates":[47],"local":[48],"details,":[49],"regional":[50],"global":[52],"analysis,":[53],"solves":[55],"coupling":[58],"problem":[59],"multi-scale":[61],"in":[63,72,142,194],"complex":[64],"backgrounds;":[65],"Meanwhile,":[66],"dynamic":[67],"attention":[68],"mechanism":[69],"is":[70],"applied":[71],"detection":[74,127,146],"head":[75],"enhance":[77],"model's":[79],"ability":[80],"characterize":[82],"geometric":[84],"features":[85],"through":[88],"adaptive":[89],"sampling":[90],"point":[91],"offset":[92],"sparse":[94],"weight":[95],"allocation;":[96],"addition,":[98],"composite":[100],"loss":[101],"function":[102],"was":[103],"designed":[104],"jointly":[106],"optimize":[107],"similarity":[109],"between":[110],"aspect":[112],"ratio":[113],"bounding":[115],"boxes":[116],"Gaussian":[119],"distribution":[120],"matching":[121],"degree,":[122],"thereby":[123],"reducing":[124],"missed":[126],"rate":[128],"targets.":[131],"The":[132],"experimental":[133],"results":[134],"were":[135],"significant,":[136],"with":[137,154,173],"achieving":[139],"86.5%":[140],"mAP":[141,178],"defect":[145],"tasks,":[147],"significantly":[148],"exceeding":[149],"comparison":[151],"model.":[152],"Compared":[153,172],"Swin-T,":[155],"accuracy":[157],"has":[158,168,179],"increased":[159,169,180],"by":[160,170,181],"11.9":[161],"percentage":[162],"points":[163],"inference":[166],"speed":[167],"132%;":[171],"YOLOv5,":[174],"YOLOv8,":[175],"YOLOv11,":[177],"11.2%,":[182],"8.7%,":[183],"6.3%,":[185],"respectively,":[186],"fully":[187],"verifying":[188],"superiority":[190],"objective":[196],"optimization":[198],"overall":[200],"performance.":[201]},"counts_by_year":[],"updated_date":"2026-03-25T14:56:36.534964","created_date":"2026-03-12T00:00:00"}