{"id":"https://openalex.org/W4407467076","doi":"https://doi.org/10.1109/iccvit63928.2024.10872413","title":"Deep Learning-Based Method for Detecting Surface Defects in Air Filters","display_name":"Deep Learning-Based Method for Detecting Surface Defects in Air Filters","publication_year":2024,"publication_date":"2024-11-24","ids":{"openalex":"https://openalex.org/W4407467076","doi":"https://doi.org/10.1109/iccvit63928.2024.10872413"},"language":"en","primary_location":{"id":"doi:10.1109/iccvit63928.2024.10872413","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvit63928.2024.10872413","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100451654","display_name":"Ying Yuan","orcid":"https://orcid.org/0000-0003-4026-4388"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Yuan","raw_affiliation_strings":["University of Electronic Science and Technology of China,Chengdu,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036698641","display_name":"Yajun Lin","orcid":"https://orcid.org/0000-0002-4425-2344"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yajun Lin","raw_affiliation_strings":["University of Electronic Science and Technology of China,Chengdu,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081403843","display_name":"Tianliang Zhang","orcid":"https://orcid.org/0000-0002-8204-559X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianliang Zhang","raw_affiliation_strings":["University of Electronic Science and Technology of China,Chengdu,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150229711"],"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":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13928","display_name":"Advanced Sensor Technologies Research","score":0.8690999746322632,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T13928","display_name":"Advanced Sensor Technologies Research","score":0.8690999746322632,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10511","display_name":"High voltage insulation and dielectric phenomena","score":0.8543999791145325,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12163","display_name":"Aerosol Filtration and Electrostatic Precipitation","score":0.8539000153541565,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/computer-science","display_name":"Computer science","score":0.6144188642501831},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5488297939300537},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5067344307899475}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6144188642501831},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5488297939300537},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5067344307899475}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccvit63928.2024.10872413","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvit63928.2024.10872413","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W234388709","https://openalex.org/W1901129140","https://openalex.org/W1981759979","https://openalex.org/W2468676150","https://openalex.org/W2768955070","https://openalex.org/W2791709739","https://openalex.org/W2884195426","https://openalex.org/W2888407265","https://openalex.org/W2953989358","https://openalex.org/W2963073614","https://openalex.org/W2963156262","https://openalex.org/W3200904534","https://openalex.org/W4315434857","https://openalex.org/W4376626035","https://openalex.org/W6751494907"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W3086377361"],"abstract_inverted_index":{"To":[0],"prevent":[1],"defective":[2,129],"air":[3,31,43],"filters":[4],"from":[5],"reaching":[6],"the":[7,55,71,86,102,106,113,123],"market,":[8],"it":[9],"is":[10],"essential":[11],"to":[12,39],"conduct":[13],"defect":[14,51,119],"inspections":[15],"on":[16],"their":[17],"appearance.":[18],"This":[19],"paper":[20],"proposes":[21],"a":[22,35,46,50,65,77],"deep":[23],"learning-based":[24],"method":[25,115],"for":[26],"detecting":[27],"surface":[28],"defects":[29,91,100],"in":[30,101,122],"filters.":[32],"We":[33],"utilize":[34],"linear":[36],"array":[37],"camera":[38],"capture":[40],"images":[41],"of":[42,85,105,125],"filters,":[44],"constructing":[45],"dataset":[47],"and":[48,59,73,89,97,130],"designing":[49],"detection":[52,67],"algorithm.":[53],"For":[54],"outer":[56,74],"metal":[57,87],"mesh":[58,88],"inner":[60,72],"filter":[61,107],"material,":[62],"we":[63],"propose":[64],"multistage":[66],"approach:":[68],"(1)":[69],"segmenting":[70],"structures":[75],"using":[76],"style":[78],"transfer":[79],"model;":[80],"(2)":[81],"performing":[82],"defect-free":[83],"reconstruction":[84],"localizing":[90,99],"through":[92],"residual":[93],"analysis;":[94],"(3)":[95],"classifying":[96],"pixel-level":[98,118],"connected":[103],"regions":[104],"material.":[108],"Experimental":[109],"results":[110],"demonstrate":[111],"that":[112],"proposed":[114],"effectively":[116],"achieves":[117],"localization":[120],"even":[121],"presence":[124],"an":[126],"imbalance":[127],"between":[128],"non-defective":[131],"data.":[132]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}