{"id":"https://openalex.org/W7163402850","doi":"https://doi.org/10.48550/arxiv.2606.03508","title":"Structure-Guided Mixed Masked Pretraining and Spatial Continuity Regularization for Printed Circuit Board Defect Detection","display_name":"Structure-Guided Mixed Masked Pretraining and Spatial Continuity Regularization for Printed Circuit Board Defect Detection","publication_year":2026,"publication_date":"2026-06-02","ids":{"openalex":"https://openalex.org/W7163402850","doi":"https://doi.org/10.48550/arxiv.2606.03508"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.03508","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03508","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.03508","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137766530","display_name":"Peitong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Peitong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102959387","display_name":"Nuo Wang","orcid":"https://orcid.org/0000-0002-2437-8759"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Nuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137795469","display_name":"Enxin Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Enxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084912833","display_name":"Chengjin Yu","orcid":"https://orcid.org/0000-0003-2544-3142"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Chengjin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102779920","display_name":"Hanyu Xuan","orcid":"https://orcid.org/0000-0002-5729-843X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xuan, Hanyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137790836","display_name":"Yuanting Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Yuanting","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.8930000066757202,"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":0.8930000066757202,"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":0.05700000002980232,"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":0.010400000028312206,"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-board","display_name":"Printed circuit board","score":0.7937999963760376},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6348999738693237},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.49480000138282776},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4575999975204468},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4237000048160553},{"id":"https://openalex.org/keywords/masking","display_name":"Masking (illustration)","score":0.42010000348091125},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.37959998846054077},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.3709000051021576}],"concepts":[{"id":"https://openalex.org/C120793396","wikidata":"https://www.wikidata.org/wiki/Q173350","display_name":"Printed circuit board","level":2,"score":0.7937999963760376},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6653000116348267},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6348999738693237},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5304999947547913},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.49480000138282776},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4575999975204468},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4237000048160553},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.42010000348091125},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4007999897003174},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.37959998846054077},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3709000051021576},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3321000039577484},{"id":"https://openalex.org/C530198007","wikidata":"https://www.wikidata.org/wiki/Q80831","display_name":"Integrated circuit","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.31380000710487366},{"id":"https://openalex.org/C134146338","wikidata":"https://www.wikidata.org/wiki/Q1815901","display_name":"Electronic circuit","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30140000581741333},{"id":"https://openalex.org/C2777042071","wikidata":"https://www.wikidata.org/wiki/Q6509304","display_name":"Leakage (economics)","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.28349998593330383},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C202374169","wikidata":"https://www.wikidata.org/wiki/Q124291","display_name":"Electrical conductor","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.03508","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03508","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.03508","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03508","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Printed":[0],"circuit":[1,31],"board":[2],"(PCB)":[3],"defect":[4,43,126,151,160,199],"detection":[5,44,127,200],"is":[6,77,121,137],"an":[7],"essential":[8],"part":[9],"of":[10,192],"automated":[11],"optical":[12],"inspection":[13],"(AOI);":[14],"yet":[15],"it":[16],"remains":[17],"challenging":[18],"in":[19,29,201],"practice":[20],"because":[21],"many":[22],"defects":[23],"are":[24],"tiny,":[25],"low-contrast,":[26],"and":[27,95,153,175,185],"embedded":[28],"dense":[30],"backgrounds.":[32],"To":[33],"address":[34],"these":[35],"issues,":[36],"this":[37],"paper":[38],"presents":[39],"a":[40,62,132],"two-phase":[41],"PCB":[42,71,103,111,198],"framework":[45,195],"that":[46,168],"combines":[47],"structure-guided":[48,74],"mixed":[49,75],"masked":[50,65,82,93],"pretraining":[51,58,66],"with":[52],"spatial":[53,133],"continuity":[54,134],"regularization.":[55],"In":[56,114],"the":[57,97,115,118,124,130,149,164,169,190,193],"stage,":[59,117],"we":[60],"design":[61],"sparse":[63,85],"convolutional":[64,86],"scheme":[67],"to":[68,79,100,123,148],"exploit":[69],"unlabeled":[70],"images,":[72],"where":[73],"masking":[76],"used":[78],"construct":[80],"informative":[81],"inputs.":[83],"The":[84],"reconstruction":[87],"pipeline":[88],"suppresses":[89],"invalid":[90],"responses":[91],"from":[92,105],"regions":[94],"enables":[96],"detector":[98],"backbone":[99,120],"infer":[101],"missing":[102],"structures":[104],"visible":[106],"conductive":[107],"patterns,":[108],"thereby":[109],"learning":[110],"structural":[112],"priors.":[113],"fine-tuning":[116],"pretrained":[119],"transferred":[122],"downstream":[125],"task.":[128],"For":[129],"task,":[131],"regularization":[135],"term":[136,142],"introduced":[138],"during":[139],"fine-tuning.":[140],"This":[141],"constrains":[143],"dispersed":[144],"positive":[145],"predictions":[146],"assigned":[147],"same":[150],"instance":[152],"promotes":[154],"more":[155],"compact":[156],"localization":[157],"on":[158,163],"elongated":[159],"regions.":[161],"Experiments":[162],"DsPCBSD+":[165],"dataset":[166],"show":[167],"proposed":[170,194],"method":[171],"achieves":[172],"85.5%":[173],"mAP0.5":[174],"52.3%":[176],"mAP0.5:0.95,":[177],"outperforming":[178],"several":[179],"strong":[180],"baseline":[181],"detectors.":[182],"Ablation":[183],"studies":[184],"qualitative":[186],"results":[187],"further":[188],"confirm":[189],"effectiveness":[191],"for":[196],"robust":[197],"industrial":[202],"AOI":[203],"scenarios.":[204]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-04T00:00:00"}
