{"id":"https://openalex.org/W7116978700","doi":"https://doi.org/10.1093/jcde/qwaf138","title":"RAGA-YOLO: Enhancing global structural perception for accurate and efficient surface defect detection","display_name":"RAGA-YOLO: Enhancing global structural perception for accurate and efficient surface defect detection","publication_year":2025,"publication_date":"2025-12-20","ids":{"openalex":"https://openalex.org/W7116978700","doi":"https://doi.org/10.1093/jcde/qwaf138"},"language":"en","primary_location":{"id":"doi:10.1093/jcde/qwaf138","is_oa":true,"landing_page_url":"https://doi.org/10.1093/jcde/qwaf138","pdf_url":"https://academic.oup.com/jcde/advance-article-pdf/doi/10.1093/jcde/qwaf138/66098231/qwaf138.pdf","source":{"id":"https://openalex.org/S2485147958","display_name":"Journal of Computational Design and Engineering","issn_l":"2288-4300","issn":["2288-4300","2288-5048"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Design and Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://academic.oup.com/jcde/advance-article-pdf/doi/10.1093/jcde/qwaf138/66098231/qwaf138.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121075134","display_name":"Tianyi Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I2898894","display_name":"Liaoning University of Technology","ror":"https://ror.org/05ay23762","country_code":"CN","type":"education","lineage":["https://openalex.org/I2898894"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyi Zheng","raw_affiliation_strings":["School of Electronics and Information Engineering, Liaoning University of Technology , Jinzhou 121001 ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Liaoning University of Technology , Jinzhou 121001 ,","institution_ids":["https://openalex.org/I2898894"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121065358","display_name":"Ling Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I2898894","display_name":"Liaoning University of Technology","ror":"https://ror.org/05ay23762","country_code":"CN","type":"education","lineage":["https://openalex.org/I2898894"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ling Yu","raw_affiliation_strings":["School of Electronics and Information Engineering, Liaoning University of Technology , Jinzhou 121001 ,"],"raw_orcid":"https://orcid.org/0000-0002-6166-405X","affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Liaoning University of Technology , Jinzhou 121001 ,","institution_ids":["https://openalex.org/I2898894"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121023001","display_name":"Yongbao Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I2898894","display_name":"Liaoning University of Technology","ror":"https://ror.org/05ay23762","country_code":"CN","type":"education","lineage":["https://openalex.org/I2898894"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongbao Shi","raw_affiliation_strings":["School of Electronics and Information Engineering, Liaoning University of Technology , Jinzhou 121001 ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Liaoning University of Technology , Jinzhou 121001 ,","institution_ids":["https://openalex.org/I2898894"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108676636","display_name":"Fanglin Niu","orcid":null},"institutions":[{"id":"https://openalex.org/I2898894","display_name":"Liaoning University of Technology","ror":"https://ror.org/05ay23762","country_code":"CN","type":"education","lineage":["https://openalex.org/I2898894"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fanglin Niu","raw_affiliation_strings":["School of Electronics and Information Engineering, Liaoning University of Technology , Jinzhou 121001 ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Liaoning University of Technology , Jinzhou 121001 ,","institution_ids":["https://openalex.org/I2898894"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5121065358"],"corresponding_institution_ids":["https://openalex.org/I2898894"],"apc_list":{"value":1650,"currency":"USD","value_usd":1650},"apc_paid":{"value":1650,"currency":"USD","value_usd":1650},"fwci":0.7655,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.79019925,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"13","issue":"1","first_page":"324","last_page":"351"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6582000255584717,"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":0.6582000255584717,"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":0.1298999935388565,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.026499999687075615,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/upsampling","display_name":"Upsampling","score":0.7106000185012817},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.710099995136261},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.45739999413490295},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.453000009059906},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44909998774528503},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.42410001158714294},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4074999988079071},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.3986999988555908}],"concepts":[{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.7106000185012817},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.710099995136261},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6150000095367432},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5853999853134155},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.45739999413490295},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.453000009059906},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44909998774528503},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.42410001158714294},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4074999988079071},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4000000059604645},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3986999988555908},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.3790999948978424},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2946999967098236},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2687000036239624},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C2778579903","wikidata":"https://www.wikidata.org/wiki/Q637321","display_name":"Washer","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1093/jcde/qwaf138","is_oa":true,"landing_page_url":"https://doi.org/10.1093/jcde/qwaf138","pdf_url":"https://academic.oup.com/jcde/advance-article-pdf/doi/10.1093/jcde/qwaf138/66098231/qwaf138.pdf","source":{"id":"https://openalex.org/S2485147958","display_name":"Journal of Computational Design and Engineering","issn_l":"2288-4300","issn":["2288-4300","2288-5048"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Design and Engineering","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1093/jcde/qwaf138","is_oa":true,"landing_page_url":"https://doi.org/10.1093/jcde/qwaf138","pdf_url":"https://academic.oup.com/jcde/advance-article-pdf/doi/10.1093/jcde/qwaf138/66098231/qwaf138.pdf","source":{"id":"https://openalex.org/S2485147958","display_name":"Journal of Computational Design and Engineering","issn_l":"2288-4300","issn":["2288-4300","2288-5048"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Design and Engineering","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.5492123365402222,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G3657393250","display_name":null,"funder_award_id":"LJKZ0624","funder_id":"https://openalex.org/F4320324560","funder_display_name":"Department of Education of Liaoning Province"},{"id":"https://openalex.org/G6581490634","display_name":null,"funder_award_id":"LJKMZ20220965","funder_id":"https://openalex.org/F4320324560","funder_display_name":"Department of Education of Liaoning Province"},{"id":"https://openalex.org/G7080568430","display_name":null,"funder_award_id":"LJ212410154028","funder_id":"https://openalex.org/F4320324560","funder_display_name":"Department of Education of Liaoning Province"}],"funders":[{"id":"https://openalex.org/F4320324560","display_name":"Department of Education of Liaoning Province","ror":"https://ror.org/0022v2454"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7116978700.pdf","grobid_xml":"https://content.openalex.org/works/W7116978700.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2193145675","https://openalex.org/W2752782242","https://openalex.org/W2798898057","https://openalex.org/W2944303778","https://openalex.org/W2952122856","https://openalex.org/W3009635072","https://openalex.org/W3012374719","https://openalex.org/W3034580371","https://openalex.org/W3034971973","https://openalex.org/W3116271762","https://openalex.org/W3177052299","https://openalex.org/W4297676427","https://openalex.org/W4310428497","https://openalex.org/W4366400469","https://openalex.org/W4386076325","https://openalex.org/W4386976814","https://openalex.org/W4388890656","https://openalex.org/W4392244943","https://openalex.org/W4393308750","https://openalex.org/W4398810114","https://openalex.org/W4402568614","https://openalex.org/W4403448018","https://openalex.org/W4403770406","https://openalex.org/W4403919744","https://openalex.org/W4403965466","https://openalex.org/W4404132945","https://openalex.org/W4404303854","https://openalex.org/W4407313908","https://openalex.org/W4407759488","https://openalex.org/W4408852291","https://openalex.org/W4409593965","https://openalex.org/W4415127320","https://openalex.org/W6948204681"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Surface":[1,160,199,209],"defect":[2,36],"detection":[3,46,133],"plays":[4],"a":[5,44,57,125,137,167],"critical":[6],"role":[7],"in":[8,15,34,170,251],"quality":[9],"control":[10],"of":[11,104,121,187,212,247],"industrial":[12,232],"products,":[13],"particularly":[14],"steel":[16],"manufacturing.":[17],"However,":[18],"existing":[19],"deep":[20],"learning":[21],"methods":[22],"often":[23],"suffer":[24],"from":[25],"high":[26],"computational":[27],"costs,":[28],"limited":[29],"generalization,":[30],"and":[31,75,107,117,146,179,183,207,221],"constrained":[32],"accuracy":[33],"complex":[35],"scenarios.":[37],"To":[38],"address":[39],"these":[40],"challenges,":[41],"we":[42],"propose":[43],"lightweight":[45,59],"model,":[47],"RAGA-YOLO,":[48],"based":[49],"on":[50,196,238],"an":[51],"improved":[52],"YOLOv11-n.":[53],"The":[54],"model":[55],"incorporates":[56],"novel":[58],"neck":[60],"structure,":[61],"CCFM-PAN-FPN,":[62],"which":[63,141],"combines":[64],"channel":[65],"alignment":[66],"with":[67,202],"efficient":[68],"downsampling":[69],"to":[70,85,95,135,151,223],"significantly":[71],"reduce":[72],"both":[73],"Params":[74],"GFLOPs.":[76,194],"Additionally,":[77],"the":[78,92,100,105,108,119,132,143,148,156,185,197,239,244,248],"Relation-aware":[79],"Global":[80],"Attention":[81],"module":[82],"is":[83,129],"introduced":[84],"enhance":[86],"global":[87],"information":[88],"modeling,":[89],"thereby":[90],"improving":[91],"model\u2019s":[93,149],"ability":[94,150],"detect":[96],"cross-regional":[97],"defects.":[98,154,254],"Moreover,":[99],"C3kDC_FFM":[101],"module,":[102],"composed":[103],"DCbottleneck":[106],"Feature":[109],"Fusion":[110],"Module":[111],"(FFM),":[112],"effectively":[113],"enhances":[114,147],"contextual":[115],"representation":[116],"improves":[118],"integration":[120],"multi-scale":[122],"features.":[123],"Furthermore,":[124],"dilated":[126],"convolution":[127],"layer":[128],"incorporated":[130],"into":[131],"head":[134,250],"form":[136],"new":[138],"Dilated":[139,249],"head,":[140],"enlarges":[142],"receptive":[144],"field":[145],"recognize":[152],"small-scale":[153,253],"On":[155],"Northeastern":[157],"University":[158],"Steel":[159],"Defect":[161,200,211],"Detection":[162,178,201],"(NEU-DET)":[163],"dataset,":[164],"RAGA-YOLO":[165],"achieves":[166],"2.1%":[168],"increase":[169],"mAP,":[171],"substantially":[172],"reduces":[173],"multiple":[174],"Toolkit":[175],"for":[176,230],"Identifying":[177],"segmentation":[180],"Errors":[181],"(TIDE),":[182],"decreases":[184],"number":[186],"parameters":[188],"by":[189],"15.3%":[190],"while":[191],"maintaining":[192],"unchanged":[193],"Experiments":[195],"Metallic":[198],"10":[203],"classes":[204],"(GC10-DET)":[205],"dataset":[206,214,241],"Anomaly":[208],"Visual":[210],"washer":[213],"further":[215,242],"demonstrate":[216],"its":[217,227],"excellent":[218],"generalization":[219],"capability":[220],"robustness":[222],"scale":[224],"variations,":[225],"highlighting":[226],"promising":[228],"potential":[229],"practical":[231],"applications.":[233],"In":[234],"addition,":[235],"experiments":[236],"conducted":[237],"PCB":[240],"verify":[243],"superior":[245],"performance":[246],"recognizing":[252]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-23T06:36:01.041984","created_date":"2025-12-23T00:00:00"}
