{"id":"https://openalex.org/W7154700053","doi":"https://doi.org/10.1016/j.array.2026.100833","title":"Automated brake caliper identification via multi-view deep learning for automotive applications","display_name":"Automated brake caliper identification via multi-view deep learning for automotive applications","publication_year":2026,"publication_date":"2026-04-17","ids":{"openalex":"https://openalex.org/W7154700053","doi":"https://doi.org/10.1016/j.array.2026.100833"},"language":"en","primary_location":{"id":"doi:10.1016/j.array.2026.100833","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.array.2026.100833","pdf_url":null,"source":{"id":"https://openalex.org/S4210194039","display_name":"Array","issn_l":"2590-0056","issn":["2590-0056"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Array","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1016/j.array.2026.100833","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078000296","display_name":"Matej Valek","orcid":null},"institutions":[{"id":"https://openalex.org/I187293425","display_name":"University of Ostrava","ror":"https://ror.org/00pyqav47","country_code":"CZ","type":"education","lineage":["https://openalex.org/I187293425"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Mat\u011bj V\u00e1lek","raw_affiliation_strings":["Department of Informatics and Computers, Faculty of science, University of Ostrava, st. 30. dubna 22, 701 03 Ostrava, Czech Republic"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informatics and Computers, Faculty of science, University of Ostrava, st. 30. dubna 22, 701 03 Ostrava, Czech Republic","institution_ids":["https://openalex.org/I187293425"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087274656","display_name":"Martin Kotyrba","orcid":"https://orcid.org/0000-0003-3780-3053"},"institutions":[{"id":"https://openalex.org/I187293425","display_name":"University of Ostrava","ror":"https://ror.org/00pyqav47","country_code":"CZ","type":"education","lineage":["https://openalex.org/I187293425"]}],"countries":["CZ"],"is_corresponding":true,"raw_author_name":"Martin Kotyrba","raw_affiliation_strings":["Department of Informatics and Computers, Faculty of science, University of Ostrava, st. 30. dubna 22, 701 03 Ostrava, Czech Republic"],"raw_orcid":"https://orcid.org/0000-0003-3780-3053","affiliations":[{"raw_affiliation_string":"Department of Informatics and Computers, Faculty of science, University of Ostrava, st. 30. dubna 22, 701 03 Ostrava, Czech Republic","institution_ids":["https://openalex.org/I187293425"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089446348","display_name":"Eva Voln\u00e1","orcid":"https://orcid.org/0000-0002-8453-3757"},"institutions":[{"id":"https://openalex.org/I187293425","display_name":"University of Ostrava","ror":"https://ror.org/00pyqav47","country_code":"CZ","type":"education","lineage":["https://openalex.org/I187293425"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Eva Voln\u00e1","raw_affiliation_strings":["Department of Informatics and Computers, Faculty of science, University of Ostrava, st. 30. dubna 22, 701 03 Ostrava, Czech Republic"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informatics and Computers, Faculty of science, University of Ostrava, st. 30. dubna 22, 701 03 Ostrava, Czech Republic","institution_ids":["https://openalex.org/I187293425"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5133871359","display_name":"Martin Pavl\u00ed\u010dek","orcid":null},"institutions":[{"id":"https://openalex.org/I187293425","display_name":"University of Ostrava","ror":"https://ror.org/00pyqav47","country_code":"CZ","type":"education","lineage":["https://openalex.org/I187293425"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Martin Pavl\u00ed\u010dek","raw_affiliation_strings":["Department of Informatics and Computers, Faculty of science, University of Ostrava, st. 30. dubna 22, 701 03 Ostrava, Czech Republic"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informatics and Computers, Faculty of science, University of Ostrava, st. 30. dubna 22, 701 03 Ostrava, Czech Republic","institution_ids":["https://openalex.org/I187293425"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5087274656"],"corresponding_institution_ids":["https://openalex.org/I187293425"],"apc_list":{"value":1350,"currency":"USD","value_usd":1350},"apc_paid":{"value":1350,"currency":"USD","value_usd":1350},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.84661433,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"30","issue":null,"first_page":"100833","last_page":"100833"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12197","display_name":"Brake Systems and Friction Analysis","score":0.3427000045776367,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T12197","display_name":"Brake Systems and Friction Analysis","score":0.3427000045776367,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T10805","display_name":"Vehicle Dynamics and Control Systems","score":0.1662999987602234,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.09200000017881393,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/calipers","display_name":"Calipers","score":0.920199990272522},{"id":"https://openalex.org/keywords/brake","display_name":"Brake","score":0.6766999959945679},{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.5981000065803528},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5159000158309937},{"id":"https://openalex.org/keywords/usable","display_name":"USable","score":0.4659000039100647},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.4546999931335449},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.45419999957084656},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.3962000012397766}],"concepts":[{"id":"https://openalex.org/C186738849","wikidata":"https://www.wikidata.org/wiki/Q12408","display_name":"Calipers","level":2,"score":0.920199990272522},{"id":"https://openalex.org/C2780999251","wikidata":"https://www.wikidata.org/wiki/Q17022503","display_name":"Brake","level":2,"score":0.6766999959945679},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6093000173568726},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.5981000065803528},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.57669997215271},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5159000158309937},{"id":"https://openalex.org/C2780615836","wikidata":"https://www.wikidata.org/wiki/Q2471869","display_name":"USable","level":2,"score":0.4659000039100647},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4596000015735626},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.4546999931335449},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.45419999957084656},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.3962000012397766},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3837999999523163},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.38260000944137573},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3564000129699707},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.33660000562667847},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.298799991607666},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2946000099182129},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29170000553131104},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.2761000096797943},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.array.2026.100833","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.array.2026.100833","pdf_url":null,"source":{"id":"https://openalex.org/S4210194039","display_name":"Array","issn_l":"2590-0056","issn":["2590-0056"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Array","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.array.2026.100833","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.array.2026.100833","pdf_url":null,"source":{"id":"https://openalex.org/S4210194039","display_name":"Array","issn_l":"2590-0056","issn":["2590-0056"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Array","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6525055766105652}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1644641054","https://openalex.org/W2194775991","https://openalex.org/W2782812883","https://openalex.org/W2798998662","https://openalex.org/W2799162093","https://openalex.org/W2950212750","https://openalex.org/W3009635072","https://openalex.org/W3035541121","https://openalex.org/W3119049870","https://openalex.org/W3120237795","https://openalex.org/W3204568647","https://openalex.org/W4319155813","https://openalex.org/W4389783117","https://openalex.org/W4391093457","https://openalex.org/W4411244563"],"related_works":[],"abstract_inverted_index":{"This":[0],"work":[1],"deals":[2],"with":[3,88],"the":[4,16,49,61,67,70,101,154,173,180,200,207,210,214],"development":[5],"of":[6,12,24,42,63,69,156,166,175,183,209,223,266],"a":[7,28,163,192],"tool":[8],"for":[9,172,195,203,217,235,245],"automated":[10],"identification":[11],"brake":[13,25,57,225,247],"calipers":[14,26,58],"in":[15,32,60,66,93,128,143,274],"automotive":[17,197],"industry.":[18],"Specifically,":[19],"we":[20],"propose":[21],"multi-view":[22,79,97,109,115,184,221,246,258],"classification":[23,121],"using":[27],"newly":[29],"created":[30,234],"dataset":[31,211,222],"which":[33],"each":[34,41],"caliper":[35,226,248],"is":[36,52,158,189,290],"captured":[37,228],"from":[38,199,229,283],"three":[39,230],"angles,":[40],"120\u00b0.":[43],"The":[44,160,186,256],"experimental":[45],"results":[46],"show":[47,113,138],"that":[48,114],"proposed":[50,257],"method":[51],"able":[53],"to":[54,78,99,118,131,141,150,206,213,287],"accurately":[55],"identify":[56],"even":[59],"presence":[62],"small":[64],"variations":[65],"shape":[68],"calipers.":[71],"We":[72],"systematically":[73],"analyzed":[74],"five":[75],"representative":[76],"approaches":[77,137],"classification:":[80],"(1)":[81],"ResNet":[82],"+":[83],"LSTM,":[84],"(2)":[85],"CNN":[86],"architecture":[87,188],"late":[89],"fusion;":[90],"(3,4)":[91],"CMAL":[92],"both":[94],"single":[95],"and":[96,105,107,169,253,264],"variants":[98],"capture":[100,285],"interaction":[102],"between":[103,134],"layers":[104],"views;":[106],"(5)":[108],"Vision":[110,259],"Transformers.":[111],"Experiments":[112],"modeling":[116],"leads":[117],"consistently":[119],"good":[120],"results,":[122],"but":[123],"individual":[124],"methods":[125,168],"differ":[126],"significantly":[127],"their":[129],"ability":[130],"exploit":[132],"coherence":[133],"views.":[135],"Attention-based":[136,269],"higher":[139],"robustness":[140],"changes":[142],"viewing":[144],"angle,":[145],"while":[146],"recurrent":[147,272],"fusion":[148,273],"tends":[149],"degrade":[151],"performance":[152],"when":[153],"order":[155],"views":[157],"ambiguous.":[159],"study":[161],"offers":[162],"critical":[164],"comparison":[165],"current":[167],"provides":[170],"conclusions":[171],"design":[174],"future":[176],"architectures":[177,242],"aimed":[178],"at":[179],"effective":[181],"use":[182],"data.":[185],"entire":[187],"designed":[190],"as":[191],"usable":[193],"solution":[194],"real-world":[196],"needs,":[198],"physical":[201],"station":[202,286],"capturing":[204],"images,":[205],"creation":[208],"itself,":[212],"software":[215],"pipeline":[216,282],"classification.":[218,237],"\u2022":[219,238,255,268,278],"A":[220,279],"65":[224],"classes":[227],"120\u00b0":[231],"angles":[232],"was":[233],"industrial":[236],"Five":[239],"deep":[240],"learning":[241],"are":[243],"compared":[244],"recognition,":[249],"including":[250],"CNN,":[251],"CMAL,":[252],"ViT.":[254],"Transformer":[260],"achieves":[261],"99.51%":[262],"accuracy":[263],"F1-score":[265],"1.00.":[267],"models":[270],"outperform":[271],"exploiting":[275],"view":[276],"coherence.":[277],"complete":[280],"end-to-end":[281],"image":[284],"deployed":[288],"classifier":[289],"presented.":[291]},"counts_by_year":[],"updated_date":"2026-04-29T09:16:38.111599","created_date":"2026-04-18T00:00:00"}
