{"id":"https://openalex.org/W4416749163","doi":"https://doi.org/10.1109/iros60139.2025.11246867","title":"IoU-Aware Clustering for Anchor Configuration Determination in Efficient Defect Detection","display_name":"IoU-Aware Clustering for Anchor Configuration Determination in Efficient Defect Detection","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416749163","doi":"https://doi.org/10.1109/iros60139.2025.11246867"},"language":null,"primary_location":{"id":"doi:10.1109/iros60139.2025.11246867","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11246867","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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/A5113347586","display_name":"Yuhao Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuhao Zhao","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences,State Key Laboratory of Multimodal Artificial Intelligence Systems,Beijing,China,100190"],"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences,State Key Laboratory of Multimodal Artificial Intelligence Systems,Beijing,China,100190","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022867743","display_name":"Hongxuan Ma","orcid":"https://orcid.org/0000-0001-6422-1441"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongxuan Ma","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences,State Key Laboratory of Multimodal Artificial Intelligence Systems,Beijing,China,100190"],"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences,State Key Laboratory of Multimodal Artificial Intelligence Systems,Beijing,China,100190","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108286207","display_name":"Wei Zou","orcid":"https://orcid.org/0000-0003-4215-5361"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zou","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences,State Key Laboratory of Multimodal Artificial Intelligence Systems,Beijing,China,100190"],"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences,State Key Laboratory of Multimodal Artificial Intelligence Systems,Beijing,China,100190","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025655969","display_name":"Zhe Liu","orcid":"https://orcid.org/0000-0001-5636-790X"},"institutions":[{"id":"https://openalex.org/I2802541053","display_name":"Academy of Military Medical Sciences","ror":"https://ror.org/02bv3c993","country_code":"CN","type":"facility","lineage":["https://openalex.org/I2802541053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Liu","raw_affiliation_strings":["Academy of Military Sciences,Chemical Defense Institute,Beijing,China,102205"],"affiliations":[{"raw_affiliation_string":"Academy of Military Sciences,Chemical Defense Institute,Beijing,China,102205","institution_ids":["https://openalex.org/I2802541053"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050531685","display_name":"Hu Su","orcid":"https://orcid.org/0000-0002-0551-3193"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hu Su","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences,State Key Laboratory of Multimodal Artificial Intelligence Systems,Beijing,China,100190"],"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences,State Key Laboratory of Multimodal Artificial Intelligence Systems,Beijing,China,100190","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100394717","display_name":"Song Liu","orcid":"https://orcid.org/0009-0008-7930-458X"},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Song Liu","raw_affiliation_strings":["ShanghaiTech University,School of Information Science and Technology,Shanghai,China,201210"],"affiliations":[{"raw_affiliation_string":"ShanghaiTech University,School of Information Science and Technology,Shanghai,China,201210","institution_ids":["https://openalex.org/I30809798"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5113347586"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.51404033,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"9995","last_page":"10001"},"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":0.7520999908447266,"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":0.7520999908447266,"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":0.19499999284744263,"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/T13049","display_name":"Surface Roughness and Optical Measurements","score":0.007899999618530273,"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/cluster-analysis","display_name":"Cluster analysis","score":0.7889000177383423},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.667900025844574},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.460999995470047},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4537999927997589},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.4081000089645386},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4074000120162964}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7889000177383423},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.667900025844574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6291000247001648},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5130000114440918},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.460999995470047},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4537999927997589},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.4081000089645386},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4074000120162964},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.36059999465942383},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.36039999127388},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35350000858306885},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3089999854564667},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3070000112056732},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros60139.2025.11246867","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11246867","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1995936107","https://openalex.org/W2108598243","https://openalex.org/W2134858030","https://openalex.org/W2570343428","https://openalex.org/W2784050770","https://openalex.org/W2897772777","https://openalex.org/W2944303778","https://openalex.org/W2952122856","https://openalex.org/W2963307811","https://openalex.org/W2963351448","https://openalex.org/W2982770724","https://openalex.org/W3006093640","https://openalex.org/W3009635072","https://openalex.org/W3046447707","https://openalex.org/W3096609285","https://openalex.org/W3107473354","https://openalex.org/W3116815602","https://openalex.org/W3126340101","https://openalex.org/W3176378302","https://openalex.org/W3185486439","https://openalex.org/W3199093552","https://openalex.org/W4288375226","https://openalex.org/W4385486449","https://openalex.org/W4389665622","https://openalex.org/W4403678661","https://openalex.org/W4405786346"],"related_works":[],"abstract_inverted_index":{"Deep-learning-based":[0],"object":[1],"detection":[2],"has":[3,71],"gained":[4],"widespread":[5],"application":[6],"in":[7],"surface":[8],"defect":[9,155,171],"inspection,":[10],"with":[11,23,46,117,173],"anchor-based":[12],"detectors":[13],"achieving":[14],"remarkable":[15],"success":[16],"by":[17,94,164],"utilizing":[18],"dense":[19],"anchors":[20],"to":[21,140,154],"align":[22],"defects.":[24,100],"Determining":[25],"the":[26,65,83,103,118,122,132,159],"optimal":[27],"anchor":[28,36,68,92,133,151],"configuration,":[29],"i.e.,":[30],"sizes":[31],"and":[32,62,111,166],"aspect":[33],"ratios":[34],"of":[35,67,121],"boxes,":[37],"remains":[38],"a":[39,113,129],"critical":[40],"challenge,":[41],"particularly":[42],"when":[43],"addressing":[44],"defects":[45],"significant":[47],"shape":[48,96,119,175],"variations.":[49,176],"While":[50],"previous":[51],"studies":[52],"have":[53],"predominantly":[54],"focused":[55],"on":[56,168],"developing":[57],"more":[58],"efficient":[59],"network":[60],"architectures":[61],"learning":[63],"strategies,":[64],"problem":[66],"configuration":[69],"determination":[70],"not":[72],"been":[73],"thoroughly":[74],"explored.":[75],"To":[76],"address":[77],"this":[78,80],"gap,":[79],"paper":[81],"proposes":[82],"IoU-Aware":[84],"Clustering":[85],"(IAC)":[86],"algorithm,":[87],"which":[88],"autonomously":[89],"learns":[90],"suitable":[91],"configurations":[93,152],"extracting":[95],"priors":[97],"from":[98],"diverse":[99],"IAC":[101,147],"takes":[102],"training":[104,123],"bounding":[105],"boxes":[106],"as":[107],"potential":[108],"clustering":[109],"centers":[110],"selects":[112],"subset":[114],"that":[115,146],"aligns":[116],"distribution":[120],"samples.":[124],"The":[125],"algorithm":[126],"involves":[127],"only":[128],"single":[130],"hyper-parameter,":[131],"number":[134],"k,":[135],"making":[136],"it":[137],"highly":[138],"adaptable":[139],"various":[141],"scenarios.":[142],"Experimental":[143],"results":[144],"demonstrate":[145],"can":[148],"effectively":[149],"generate":[150],"tailored":[153],"shapes,":[156],"significantly":[157],"improving":[158],"mean":[160],"Average":[161],"Precision":[162],"(mAP)":[163],"6.9%":[165],"14.4%":[167],"two":[169],"industrial":[170],"datasets":[172],"substantial":[174]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-28T00:00:00"}