{"id":"https://openalex.org/W4416952623","doi":"https://doi.org/10.48550/arxiv.2511.23241","title":"Synthetic Industrial Object Detection: GenAI vs. Feature-Based Methods","display_name":"Synthetic Industrial Object Detection: GenAI vs. Feature-Based Methods","publication_year":2025,"publication_date":"2025-11-28","ids":{"openalex":"https://openalex.org/W4416952623","doi":"https://doi.org/10.48550/arxiv.2511.23241"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2511.23241","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.23241","pdf_url":"https://arxiv.org/pdf/2511.23241","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.23241","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5093052711","display_name":"Jose Moises Araya-Martinez","orcid":"https://orcid.org/0009-0006-5507-9512"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Araya-Martinez, Jose Moises","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117604699","display_name":"Adri\u00e1n Sanchis Reig","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Reig, Adri\u00e1n Sanchis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Mohan, Gautham","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohan, Gautham","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Sardari, Sarvenaz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sardari, Sarvenaz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002499102","display_name":"Jens Lambrecht","orcid":"https://orcid.org/0000-0002-1017-9548"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lambrecht, Jens","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5065074078","display_name":"J\u00f6rg Kr\u00fcger","orcid":"https://orcid.org/0000-0001-5138-0793"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kr\u00fcger, J\u00f6rg","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/T10036","display_name":"Advanced Neural Network Applications","score":0.3619000017642975,"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":0.3619000017642975,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.35670000314712524,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.05649999901652336,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.6802999973297119},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.5920000076293945},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5536999702453613},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.4537999927997589},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.3727000057697296},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.35350000858306885},{"id":"https://openalex.org/keywords/data-driven","display_name":"Data-driven","score":0.34790000319480896},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.3346000015735626}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7526000142097473},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.697700023651123},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.6802999973297119},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.5920000076293945},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5536999702453613},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.524399995803833},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.4537999927997589},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.3727000057697296},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.34790000319480896},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.2892000079154968},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.2750000059604645},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C2781411174","wikidata":"https://www.wikidata.org/wiki/Q8034662","display_name":"Workcell","level":3,"score":0.26649999618530273},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2529999911785126}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2511.23241","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.23241","pdf_url":"https://arxiv.org/pdf/2511.23241","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2511.23241","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.23241","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":"pmh:oai:arXiv.org:2511.23241","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.23241","pdf_url":"https://arxiv.org/pdf/2511.23241","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"Reducing":[0],"the":[1,13,32,77,159,170,206],"burden":[2],"of":[3,16,46,81,165,190],"data":[4,69,126,184],"generation":[5,185],"and":[6,21,50,61,79,83,114,116,140,152,167,172],"annotation":[7],"remains":[8],"a":[9,28,44,90,109,117],"major":[10],"challenge":[11],"for":[12,65,183,203],"cost-effective":[14],"deployment":[15],"machine":[17],"learning":[18],"in":[19,149],"industrial":[20,111,171],"robotics":[22,119,173],"settings.":[23],"While":[24],"synthetic":[25,68,218],"rendering":[26,63],"is":[27,130],"promising":[29],"solution,":[30],"bridging":[31,205],"sim-to-real":[33,191,207],"gap":[34],"often":[35],"requires":[36],"expert":[37],"intervention.":[38],"In":[39],"this":[40],"work,":[41],"we":[42],"benchmark":[43],"range":[45],"domain":[47,51],"randomization":[48],"(DR)":[49],"adaptation":[52],"(DA)":[53],"techniques,":[54],"including":[55],"feature-based":[56,135],"methods,":[57,136],"generative":[58],"AI":[59],"(GenAI),":[60],"classical":[62],"approaches,":[64],"creating":[66],"contextualized":[67],"without":[70],"manual":[71],"annotation.":[72],"Our":[73,198],"evaluation":[74],"focuses":[75],"on":[76,106,169,217],"effectiveness":[78],"efficiency":[80],"low-level":[82],"high-level":[84],"feature":[85],"alignment,":[86],"as":[87,89,132,138],"well":[88],"controlled":[91],"diffusion-based":[92],"DA":[93],"method":[94],"guided":[95],"by":[96],"prompts":[97],"generated":[98],"from":[99,213],"real-world":[100,211],"contexts.":[101],"We":[102],"validate":[103],"our":[104],"methods":[105,178],"two":[107],"datasets:":[108],"proprietary":[110],"dataset":[112],"(automotive":[113],"logistics)":[115],"public":[118],"dataset.":[120],"Results":[121],"show":[122],"that":[123],"if":[124],"render-based":[125],"with":[127,162],"enough":[128],"variability":[129],"available":[131],"seed,":[133],"simpler":[134,196],"such":[137],"brightness-based":[139],"perceptual":[141],"hashing":[142,156],"filtering,":[143],"outperform":[144],"more":[145],"complex":[146],"GenAI-based":[147],"approaches":[148],"both":[150],"accuracy":[151],"resource":[153],"efficiency.":[154],"Perceptual":[155],"consistently":[157],"achieves":[158],"highest":[160],"performance,":[161],"mAP50":[163],"scores":[164],"98%":[166],"67%":[168],"datasets,":[174],"respectively.":[175],"Additionally,":[176],"GenAI":[177],"present":[179],"significant":[180],"time":[181],"overhead":[182],"at":[186],"no":[187],"apparent":[188],"improvement":[189],"mAP":[192],"values":[193],"compared":[194],"to":[195],"methods.":[197],"findings":[199],"offer":[200],"actionable":[201],"insights":[202],"efficiently":[204],"gap,":[208],"enabling":[209],"high":[210],"performance":[212],"models":[214],"trained":[215],"exclusively":[216],"data.":[219]},"counts_by_year":[],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-12-03T00:00:00"}