{"id":"https://openalex.org/W4415399394","doi":"https://doi.org/10.1109/etfa65518.2025.11205591","title":"Generation of Synthetic Data for DL-based Defect Detection in the Automotive Context","display_name":"Generation of Synthetic Data for DL-based Defect Detection in the Automotive Context","publication_year":2025,"publication_date":"2025-09-09","ids":{"openalex":"https://openalex.org/W4415399394","doi":"https://doi.org/10.1109/etfa65518.2025.11205591"},"language":"en","primary_location":{"id":"doi:10.1109/etfa65518.2025.11205591","is_oa":false,"landing_page_url":"https://doi.org/10.1109/etfa65518.2025.11205591","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA)","raw_type":"proceedings-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/A5093811561","display_name":"Alexander Moriz","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Alexander Moriz","raw_affiliation_strings":["Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University,Aachen,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University,Aachen,Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008547628","display_name":"Dominik Wolfschl\u00e4ger","orcid":"https://orcid.org/0000-0003-2399-4856"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Dominik Wolfschl\u00e4ger","raw_affiliation_strings":["Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University,Aachen,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University,Aachen,Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Andrei Svetlakov","orcid":null},"institutions":[{"id":"https://openalex.org/I4210094734","display_name":"GEPRO (Germany)","ror":"https://ror.org/00vj44q65","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210094734"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andrei Svetlakov","raw_affiliation_strings":["IconPro GmbH,Aachen,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IconPro GmbH,Aachen,Germany","institution_ids":["https://openalex.org/I4210094734"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120084179","display_name":"Miguel Suchodolak","orcid":null},"institutions":[{"id":"https://openalex.org/I4210094734","display_name":"GEPRO (Germany)","ror":"https://ror.org/00vj44q65","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210094734"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Miguel Suchodolak","raw_affiliation_strings":["IconPro GmbH,Aachen,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IconPro GmbH,Aachen,Germany","institution_ids":["https://openalex.org/I4210094734"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066237208","display_name":"Achim Byl","orcid":null},"institutions":[{"id":"https://openalex.org/I4210094734","display_name":"GEPRO (Germany)","ror":"https://ror.org/00vj44q65","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210094734"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Achim Byl","raw_affiliation_strings":["IconPro GmbH,Aachen,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IconPro GmbH,Aachen,Germany","institution_ids":["https://openalex.org/I4210094734"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039509967","display_name":"Friedrich Wolf","orcid":"https://orcid.org/0000-0002-0545-5755"},"institutions":[{"id":"https://openalex.org/I40753682","display_name":"Ford (Germany)","ror":"https://ror.org/017vgxg86","country_code":"DE","type":"company","lineage":["https://openalex.org/I40753682"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Friedrich Wolf-Monheim","raw_affiliation_strings":["Ford Motor Company Ford-Werke GmbH,Cologne,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ford Motor Company Ford-Werke GmbH,Cologne,Germany","institution_ids":["https://openalex.org/I40753682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120084180","display_name":"Kai Wege","orcid":null},"institutions":[{"id":"https://openalex.org/I40753682","display_name":"Ford (Germany)","ror":"https://ror.org/017vgxg86","country_code":"DE","type":"company","lineage":["https://openalex.org/I40753682"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Kai Wege","raw_affiliation_strings":["Ford Motor Company Ford-Werke GmbH,Cologne,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ford Motor Company Ford-Werke GmbH,Cologne,Germany","institution_ids":["https://openalex.org/I40753682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037308036","display_name":"Eil\u00eds Carey","orcid":null},"institutions":[{"id":"https://openalex.org/I40753682","display_name":"Ford (Germany)","ror":"https://ror.org/017vgxg86","country_code":"DE","type":"company","lineage":["https://openalex.org/I40753682"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Eilis Carey","raw_affiliation_strings":["Ford Motor Company Ford-Werke GmbH,Cologne,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ford Motor Company Ford-Werke GmbH,Cologne,Germany","institution_ids":["https://openalex.org/I40753682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045094368","display_name":"Robert Schmitt","orcid":"https://orcid.org/0000-0002-0011-5962"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Robert H. Schmitt","raw_affiliation_strings":["Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University,Aachen,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University,Aachen,Germany","institution_ids":["https://openalex.org/I887968799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":9,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38274118,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"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":0.9959999918937683,"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":0.9959999918937683,"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/T11159","display_name":"Manufacturing Process and Optimization","score":0.954800009727478,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.7585999965667725},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6514999866485596},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6485999822616577},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5109000205993652},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.46939998865127563},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.43230000138282776},{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.3952000141143799},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3790000081062317}],"concepts":[{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.7585999965667725},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6532999873161316},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6514999866485596},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6485999822616577},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5109000205993652},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4927000105381012},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.46939998865127563},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.43230000138282776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3986999988555908},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3790000081062317},{"id":"https://openalex.org/C71745522","wikidata":"https://www.wikidata.org/wiki/Q2476929","display_name":"Confidentiality","level":2,"score":0.36970001459121704},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.3569999933242798},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3440000116825104},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2994000017642975},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2766000032424927},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.26809999346733093},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.26339998841285706},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.2612999975681305},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/etfa65518.2025.11205591","is_oa":false,"landing_page_url":"https://doi.org/10.1109/etfa65518.2025.11205591","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA)","raw_type":"proceedings-article"},{"id":"pmh:oai:publications.rwth-aachen.de:1020340","is_oa":false,"landing_page_url":"https://publications.rwth-aachen.de/record/1020340","pdf_url":null,"source":{"id":"https://openalex.org/S4306401362","display_name":"RWTH Publications (RWTH Aachen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887968799","host_organization_name":"RWTH Aachen University","host_organization_lineage":["https://openalex.org/I887968799"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA) : proceedings / EFTA 2025, Porto, Portugal, 9-12 September 2025 ; editors: Lu\u00eds Almeida, Marina Indria, M\u00e1rio de Sousa, Antonio Visioli, Mohammad Ashjaei, Pedro Santos<br/>IEEE 30. International Conference on Emerging Technologies and Factory Automation, ETFA 2025, Porto, Portugal, 2025-09-09 - 2025-09-12","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2968638474","https://openalex.org/W2994152969","https://openalex.org/W3099574934","https://openalex.org/W3111221273","https://openalex.org/W3158980972","https://openalex.org/W3165012668","https://openalex.org/W3172604455","https://openalex.org/W3197303051","https://openalex.org/W3214069043","https://openalex.org/W3215644105","https://openalex.org/W4205358826","https://openalex.org/W4214660522","https://openalex.org/W4312443091","https://openalex.org/W4390504782","https://openalex.org/W4390874575"],"related_works":[],"abstract_inverted_index":{"In":[0],"the":[1,4,32,43,65,96,104,110,161,165,180],"automotive":[2,183],"context,":[3],"quality":[5,14],"of":[6,60,98,106,112,182],"products":[7],"or":[8],"assemblies":[9],"is":[10],"inspected":[11],"regularly":[12],"at":[13],"gates,":[15],"constituting":[16],"a":[17,133,141],"laborious":[18],"and":[19,40,71,90,125,138,185],"error-prone":[20],"process":[21],"if":[22],"performed":[23],"manually.":[24],"Machine":[25],"Vision":[26],"(MV)":[27],"systems":[28,48],"can":[29],"partially":[30],"automate":[31],"inspection":[33],"processes,":[34],"relieving":[35],"workers":[36],"from":[37],"such":[38,73],"tasks":[39],"thus":[41],"increasing":[42],"overall":[44],"productivity.":[45],"Although":[46],"MV":[47],"offer":[49],"high":[50,87],"potential":[51],"in":[52,75,151,179],"theory,":[53],"their":[54],"performance":[55],"depends":[56],"on":[57,145,157],"large":[58],"volumes":[59],"annotated":[61],"data":[62,74,108,147],"specific":[63],"to":[64,82,95,154,167],"considered":[66],"use":[67],"cases.":[68],"Yet,":[69],"acquiring":[70],"annotating":[72],"industrial":[76],"settings":[77],"poses":[78],"significant":[79],"challenges":[80],"due":[81],"accessibility":[83],"constraints,":[84],"confidentiality":[85],"issues,":[86],"manual":[88,127],"effort,":[89],"costs.":[91],"As":[92],"an":[93],"alternative":[94],"acquisition":[97],"real-world":[99,158],"data,":[100],"this":[101],"work":[102],"investigates":[103],"utilization":[105],"synthetic":[107,146],"for":[109,117,135,175],"training":[111],"deep":[113],"learning":[114],"(DL)":[115],"models":[116],"defect":[118],"detection,":[119],"providing":[120,172],"high-quality":[121],"annotations":[122],"by":[123],"design":[124],"reducing":[126],"effort":[128],"significantly.":[129],"The":[130],"authors":[131],"present":[132],"methodology":[134],"image":[136],"generation":[137],"demonstrate":[139],"that":[140],"DL":[142],"model":[143],"trained":[144,156],"achieves":[148],"comparable":[149],"accuracy":[150],"object":[152],"detection":[153],"one":[155],"data.":[159],"Moreover,":[160],"presented":[162],"approach":[163],"offers":[164],"capability":[166],"generate":[168],"explicit":[169],"error":[170,176],"cases,":[171],"valuable":[173],"insights":[174],"mitigation":[177],"strategies":[178],"context":[181],"production":[184],"assembly":[186],"processes.":[187]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-22T00:00:00"}