{"id":"https://openalex.org/W4393214756","doi":"https://doi.org/10.1117/12.3026364","title":"Super-resolution knowledge-distillation-based low-resolution steel defect images classification","display_name":"Super-resolution knowledge-distillation-based low-resolution steel defect images classification","publication_year":2024,"publication_date":"2024-03-27","ids":{"openalex":"https://openalex.org/W4393214756","doi":"https://doi.org/10.1117/12.3026364"},"language":"en","primary_location":{"id":"doi:10.1117/12.3026364","doi":"https://doi.org/10.1117/12.3026364","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.3026364","pdf_url":null,"source":{"id":"https://openalex.org/S4363608512","display_name":"International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5101557084","display_name":"Shun Zhang","orcid":"https://orcid.org/0000-0003-3380-8957","observed_orcids":["https://orcid.org/0000-0003-3380-8957"]},"institutions":[{"id":"https://openalex.org/I102208913","display_name":"Shihezi University","ror":"https://ror.org/04x0kvm78","country_code":"CN","type":"education","lineage":["https://openalex.org/I102208913"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shun Zhang","raw_affiliation_strings":["Shihezi University (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shihezi University (China)","institution_ids":["https://openalex.org/I102208913"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113153310","display_name":"Zixiang Pei","orcid":"https://orcid.org/0009-0002-0685-1511","observed_orcids":["https://orcid.org/0009-0002-0685-1511"]},"institutions":[{"id":"https://openalex.org/I102208913","display_name":"Shihezi University","ror":"https://ror.org/04x0kvm78","country_code":"CN","type":"education","lineage":["https://openalex.org/I102208913"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zixiang Pei","raw_affiliation_strings":["Shihezi University (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shihezi University (China)","institution_ids":["https://openalex.org/I102208913"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102687687","display_name":"Zekai Ren","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I102208913","display_name":"Shihezi University","ror":"https://ror.org/04x0kvm78","country_code":"CN","type":"education","lineage":["https://openalex.org/I102208913"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zekai Ren","raw_affiliation_strings":["Shihezi University (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shihezi University (China)","institution_ids":["https://openalex.org/I102208913"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I102208913"],"apc_list":null,"apc_paid":null,"fwci":1.223,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.7317032,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"42","last_page":"42"},"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":0.9878000020980835,"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":0.9878000020980835,"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":0.005100000184029341,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.0020000000949949026,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/steel-surface-defect-detection","display_name":"steel surface defect detection","score":0.7960000038146973},{"id":"https://openalex.org/keywords/low-resolution-images","display_name":"low-resolution images","score":0.921999990940094},{"id":"https://openalex.org/keywords/super-resolution-reconstruction","display_name":"super-resolution reconstruction","score":0.8650000095367432},{"id":"https://openalex.org/keywords/knowledge-distillation","display_name":"knowledge distillation","score":1.0},{"id":"https://openalex.org/keywords/image-classification","display_name":"image classification","score":0.8009999990463257},{"id":"https://openalex.org/keywords/lightweight-models","display_name":"lightweight models","score":0.8489999771118164},{"id":"https://openalex.org/keywords/real-time-defect-detection","display_name":"real-time defect detection","score":0.8700000047683716}],"concepts":[{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.6667904853820801},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.6295363903045654},{"id":"https://openalex.org/C3019883945","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Low resolution","level":3,"score":0.5972287654876709},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5687500834465027},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5235702395439148},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5128234028816223},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.5067723393440247},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4883999228477478},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.39512985944747925},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34830909967422485},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2667449116706848},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.16917425394058228},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0950227677822113},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.07338955998420715},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.06839898228645325}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.3026364","doi":"https://doi.org/10.1117/12.3026364","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.3026364","pdf_url":null,"source":{"id":"https://openalex.org/S4363608512","display_name":"International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1981759979","https://openalex.org/W2067042811","https://openalex.org/W2194775991","https://openalex.org/W2944303778","https://openalex.org/W3034368386","https://openalex.org/W3096831136","https://openalex.org/W4200252498"],"related_works":["https://openalex.org/W4213095305","https://openalex.org/W7125982014","https://openalex.org/W7118008227","https://openalex.org/W4405312083","https://openalex.org/W7129055849","https://openalex.org/W7223390655","https://openalex.org/W7222921612","https://openalex.org/W7126036809","https://openalex.org/W4404408029","https://openalex.org/W4312636748"],"abstract_inverted_index":{"Due":[0],"to":[1,60,65,85],"cost":[2],"and":[3,25,70,101,171],"equipment":[4],"limitations,":[5],"steel":[6,45,140,155],"surface":[7],"defect":[8,46,141,156,168],"images":[9,24,47,82,115],"are":[10],"often":[11],"of":[12,19,30,97,104,113,122,134,153,161,180],"low":[13],"resolution,":[14],"significantly":[15],"impairing":[16],"the":[17,23,28,95,102,111,119,123,145,150,178],"recognizability":[18],"key":[20],"features":[21],"within":[22],"thereby":[26],"reducing":[27],"accuracy":[29,152],"automated":[31,167],"detection.":[32],"To":[33],"address":[34],"this":[35,37],"issue,":[36],"paper":[38],"proposes":[39],"a":[40,173],"classification":[41,151],"method":[42,54,146],"for":[43,89,128],"low-resolution":[44,62,90,114,154,181],"based":[48],"on":[49,80,137],"super-resolution":[50,58,98],"knowledge":[51,73,105],"distillation.":[52,106],"This":[53,92,158],"initially":[55],"employs":[56],"advanced":[57],"techniques":[59],"reconstruct":[61],"images,":[63],"aiming":[64],"restore":[66],"critical":[67],"image":[68,99,182],"details":[69],"features.":[71],"Subsequently,":[72],"distilled":[74],"from":[75],"deep":[76],"learning":[77],"models":[78,86],"trained":[79],"high-resolution":[81],"is":[83,160],"transferred":[84],"specifically":[87],"designed":[88],"images.":[91,157],"approach":[93],"combines":[94],"advantages":[96],"reconstruction":[100],"efficiency":[103],"It":[107],"not":[108],"only":[109],"enhances":[110,149],"quality":[112],"but":[116],"also":[117],"maintains":[118],"lightweight":[120],"nature":[121],"model,":[124],"making":[125],"it":[126],"suitable":[127],"real-time":[129],"detection":[130,169],"scenarios.":[131],"A":[132],"series":[133],"experiments":[135],"conducted":[136],"publicly":[138],"available":[139],"dataset":[142],"demonstrate":[143],"that":[144],"proposed":[147],"effectively":[148],"achievement":[159],"significant":[162],"importance":[163],"in":[164,177],"advancing":[165],"industrial":[166],"technologies":[170],"provides":[172],"new":[174],"research":[175],"direction":[176],"field":[179],"processing.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2024-03-28T00:00:00"}