{"id":"https://openalex.org/W7163206528","doi":"https://doi.org/10.48550/arxiv.2606.01962","title":"Contrastive Augmented Transformer with Domain-specific Enhancement for Robust Multi-scenario Metal Surface Defect Detection","display_name":"Contrastive Augmented Transformer with Domain-specific Enhancement for Robust Multi-scenario Metal Surface Defect Detection","publication_year":2026,"publication_date":"2026-06-01","ids":{"openalex":"https://openalex.org/W7163206528","doi":"https://doi.org/10.48550/arxiv.2606.01962"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.01962","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01962","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2606.01962","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137629100","display_name":"Yiyao Liua","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yiyao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137699581","display_name":"Wenxiao He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Wenxiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037822784","display_name":"Liyuan Ren","orcid":"https://orcid.org/0000-0002-9263-4945"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Liyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137662961","display_name":"Huan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Huan","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":0.5047000050544739,"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":0.5047000050544739,"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":0.3328000009059906,"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/T13049","display_name":"Surface Roughness and Optical Measurements","score":0.033900000154972076,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/robustness","display_name":"Robustness (evolution)","score":0.7462999820709229},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6007999777793884},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4163999855518341},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.37439998984336853},{"id":"https://openalex.org/keywords/feature-matching","display_name":"Feature matching","score":0.3562000095844269},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.32670000195503235}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7462999820709229},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6007999777793884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.569599986076355},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48089998960494995},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4163999855518341},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39910000562667847},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.37439998984336853},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.3562000095844269},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3521000146865845},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.3116999864578247},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.2879999876022339},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C19474535","wikidata":"https://www.wikidata.org/wiki/Q131172","display_name":"Welding","level":2,"score":0.26030001044273376}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.01962","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01962","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2606.01962","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01962","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":[{"id":"https://metadata.un.org/sdg/16","score":0.4893917441368103,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Metal":[0],"surface":[1,158],"defect":[2,50,80,116],"detection":[3],"is":[4],"critical":[5],"for":[6,48,151,156,163],"maintaining":[7],"product":[8],"quality":[9],"in":[10,23,114],"industrial":[11,165],"manufacturing.":[12],"However,":[13],"it":[14],"faces":[15],"significant":[16],"challenges,":[17],"including":[18,148],"limited":[19],"annotated":[20],"data,":[21],"difficulty":[22],"identifying":[24],"subtle":[25,77],"multi-scale":[26,79],"defects,":[27,153,159],"and":[28,59,78,142,154],"poor":[29],"generalization":[30,141],"across":[31],"diverse":[32],"scenarios.":[33],"To":[34,82],"address":[35],"these":[36],"issues,":[37],"this":[38],"paper":[39],"proposes":[40],"a":[41,54,91,99,128],"novel":[42],"Contrastive":[43],"Augmented":[44],"Transformer":[45,57],"(CAT)":[46],"framework":[47],"robust":[49],"detection.":[51],"CAT":[52,126,138],"employs":[53],"hierarchical":[55],"Swin":[56],"backbone":[58],"redesigns":[60],"the":[61,105,110,121],"feature":[62],"pyramid":[63],"network":[64],"to":[65,108],"effectively":[66],"fuse":[67],"low-level":[68],"textures":[69],"with":[70],"high-level":[71],"semantics,":[72],"enabling":[73],"precise":[74],"modeling":[75],"of":[76,131],"patterns.":[81],"enhance":[83],"robustness":[84,143],"under":[85],"real-world":[86],"noise":[87],"conditions,":[88],"we":[89,97],"propose":[90],"domain-specific":[92],"droplet":[93],"augmentation":[94],"algorithm.":[95],"Furthermore,":[96],"incorporate":[98],"hard":[100],"negative":[101],"mining":[102],"strategy":[103],"into":[104],"contrastive":[106],"loss":[107],"strengthen":[109],"model's":[111],"discrimination":[112],"ability":[113],"ambiguous":[115],"regions.":[117],"Experimental":[118],"results":[119],"on":[120,144],"KolektorSDD2":[122],"dataset":[123],"demonstrate":[124],"that":[125],"achieves":[127],"pixel-level":[129],"AUROC":[130],"99.54%,":[132],"outperforming":[133],"existing":[134],"methods.":[135],"In":[136],"addition,":[137],"exhibits":[139],"superior":[140],"three":[145],"unseen":[146],"datasets,":[147],"KSDD1,":[149],"MTD":[150],"tile":[152],"MSDD":[155],"rail":[157],"demonstrating":[160],"its":[161],"potential":[162],"wide-scale":[164],"deployment.":[166]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-03T00:00:00"}