{"id":"https://openalex.org/W4205295783","doi":"https://doi.org/10.1142/s0218001422520048","title":"Class Highlight Generative Adversarial Networks for Strip Steel Defect Classification","display_name":"Class Highlight Generative Adversarial Networks for Strip Steel Defect Classification","publication_year":2022,"publication_date":"2022-01-12","ids":{"openalex":"https://openalex.org/W4205295783","doi":"https://doi.org/10.1142/s0218001422520048"},"language":"en","primary_location":{"id":"doi:10.1142/s0218001422520048","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001422520048","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-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/A5026955456","display_name":"Jiang Chang","orcid":"https://orcid.org/0000-0002-2786-5712"},"institutions":[{"id":"https://openalex.org/I27599042","display_name":"Xi'an Polytechnic University","ror":"https://ror.org/03442p831","country_code":"CN","type":"education","lineage":["https://openalex.org/I27599042"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiang Chang","raw_affiliation_strings":["School of Mechanical and Electronic Engineering, Xi\u2019an Polytechnic University, Xi\u2019an, 710048, P. R. China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical and Electronic Engineering, Xi\u2019an Polytechnic University, Xi\u2019an, 710048, P. R. China","institution_ids":["https://openalex.org/I27599042"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055197997","display_name":"Shengqi Guan","orcid":"https://orcid.org/0000-0002-8316-1138"},"institutions":[{"id":"https://openalex.org/I27599042","display_name":"Xi'an Polytechnic University","ror":"https://ror.org/03442p831","country_code":"CN","type":"education","lineage":["https://openalex.org/I27599042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengqi Guan","raw_affiliation_strings":["School of Mechanical and Electronic Engineering, Xi\u2019an Polytechnic University, Xi\u2019an, 710048, P. R. China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical and Electronic Engineering, Xi\u2019an Polytechnic University, Xi\u2019an, 710048, P. R. China","institution_ids":["https://openalex.org/I27599042"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5026955456"],"corresponding_institution_ids":["https://openalex.org/I27599042"],"apc_list":null,"apc_paid":null,"fwci":0.4001,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.64979431,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"36","issue":"02","first_page":null,"last_page":null},"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":0.9962999820709229,"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":0.9962999820709229,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9721999764442444,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9685999751091003,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.8876610398292542},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.7737178206443787},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.7330118417739868},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6810892820358276},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.643824577331543},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.6382414102554321},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6339962482452393},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.559015691280365},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.550270140171051},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5469595789909363},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5429657697677612},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.506650984287262},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35005983710289},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32099151611328125},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2718062996864319}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.8876610398292542},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.7737178206443787},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.7330118417739868},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6810892820358276},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.643824577331543},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6382414102554321},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6339962482452393},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.559015691280365},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.550270140171051},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5469595789909363},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5429657697677612},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.506650984287262},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35005983710289},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32099151611328125},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2718062996864319},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218001422520048","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001422520048","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6000000238418579}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2078087367","https://openalex.org/W2194775991","https://openalex.org/W2406523001","https://openalex.org/W2593414223","https://openalex.org/W2595840341","https://openalex.org/W2894668550","https://openalex.org/W2913117896","https://openalex.org/W2963122961","https://openalex.org/W2963693541","https://openalex.org/W2964195914","https://openalex.org/W2981436300","https://openalex.org/W2989611864","https://openalex.org/W3004275510","https://openalex.org/W3007827672","https://openalex.org/W3028328926","https://openalex.org/W3035414587"],"related_works":["https://openalex.org/W3217069185","https://openalex.org/W4293400715","https://openalex.org/W3049340819","https://openalex.org/W4308928038","https://openalex.org/W4200430540","https://openalex.org/W3141413246","https://openalex.org/W3112293331","https://openalex.org/W2888032422","https://openalex.org/W2808862658","https://openalex.org/W3209610097"],"abstract_inverted_index":{"In":[0,31,134],"order":[1,32,90],"to":[2,33,91],"solve":[3],"the":[4,38,42,52,61,76,79,83,99,110,116,129,135,138,145,148,162,167,174],"problem":[5],"of":[6,41,78,112,118,141,147,166,178],"dataset":[7,130],"expansion":[8],"in":[9,89,170,173],"deep":[10],"learning":[11],"tasks":[12],"such":[13],"as":[14],"image":[15,21,35,53,80,84,101],"classification,":[16],"this":[17,171],"paper":[18,172],"proposed":[19,169],"an":[20],"generation":[22,175],"model":[23,43],"called":[24],"Class":[25],"Highlight":[26],"Generative":[27],"Adversarial":[28],"Networks":[29],"(CH-GANs).":[30],"highlight":[34],"categories,":[36,50],"accelerate":[37],"convergence":[39],"speed":[40],"and":[44,58,93,115,125,164,176],"generate":[45],"true-to-life":[46],"images":[47],"with":[48,153],"clear":[49],"first,":[51],"category":[54,85],"labels":[55],"were":[56],"deconvoluted":[57],"integrated":[59],"into":[60],"generator":[62],"through":[63],"[Formula:":[64],"see":[65,155],"text]":[66],"convolution.":[67],"Second,":[68],"a":[69],"novel":[70],"discriminator":[71],"that":[72],"cannot":[73],"only":[74],"judge":[75],"authenticity":[77],"but":[81],"also":[82],"was":[86,105,126],"designed.":[87],"Finally,":[88],"quickly":[92],"accurately":[94],"classify":[95],"strip":[96,179],"steel":[97,180],"defects,":[98],"lightweight":[100],"classification":[102,177],"network":[103,113,119],"GhostNet":[104,150],"appropriately":[106],"improved":[107,149],"by":[108,132],"modifying":[109],"number":[111,117],"layers":[114],"channels,":[120],"adding":[121],"SE":[122],"modules,":[123],"etc.,":[124],"trained":[127],"on":[128],"expanded":[131],"CH-GAN.":[133],"comparative":[136],"experiments,":[137],"average":[139],"FID":[140],"CH-GAN":[142],"is":[143,151],"7.59;":[144],"accuracy":[146],"95.67%":[152],"0.19[Formula:":[154],"text]M":[156],"parameters.":[157],"The":[158],"experimental":[159],"results":[160],"prove":[161],"effectiveness":[163],"superiority":[165],"methods":[168],"defect":[181],"images.":[182]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
