{"id":"https://openalex.org/W4376852202","doi":"https://doi.org/10.1145/3573942.3574109","title":"Steel Defect Detection Based on Yolov5","display_name":"Steel Defect Detection Based on Yolov5","publication_year":2022,"publication_date":"2022-09-23","ids":{"openalex":"https://openalex.org/W4376852202","doi":"https://doi.org/10.1145/3573942.3574109"},"language":"en","primary_location":{"id":"doi:10.1145/3573942.3574109","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3573942.3574109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","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/A5037211132","display_name":"Wanyu Deng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wanyu Deng","raw_affiliation_strings":["Xi`an University Of Posts &amp; Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-9818-5562","affiliations":[{"raw_affiliation_string":"Xi`an University Of Posts &amp; Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101937504","display_name":"Wen Ma","orcid":"https://orcid.org/0000-0003-1199-6846"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Ma","raw_affiliation_strings":["Xi`an University Of Posts &amp; Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0003-1199-6846","affiliations":[{"raw_affiliation_string":"Xi`an University Of Posts &amp; Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5037211132"],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30968216,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"880","last_page":"888"},"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":0.9993000030517578,"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":0.9993000030517578,"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/T14319","display_name":"Currency Recognition and Detection","score":0.9672999978065491,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9341999888420105,"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/computer-science","display_name":"Computer science","score":0.6503952741622925},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5777443051338196},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.542244553565979},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5193480253219604},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5005803108215332},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.4881325662136078},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4421168267726898},{"id":"https://openalex.org/keywords/production","display_name":"Production (economics)","score":0.4393239915370941},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4124610424041748},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37905287742614746},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.1668108105659485}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6503952741622925},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5777443051338196},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.542244553565979},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5193480253219604},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5005803108215332},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.4881325662136078},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4421168267726898},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.4393239915370941},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4124610424041748},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37905287742614746},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.1668108105659485},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"score":0.0},{"id":"https://openalex.org/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"score":0.0},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3573942.3574109","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3573942.3574109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Responsible consumption and production","id":"https://metadata.un.org/sdg/12"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W315411188","https://openalex.org/W639708223","https://openalex.org/W1498436455","https://openalex.org/W1952134217","https://openalex.org/W1981570852","https://openalex.org/W1995341919","https://openalex.org/W2048692651","https://openalex.org/W2092072518","https://openalex.org/W2585123518","https://openalex.org/W2905416267","https://openalex.org/W2944303778","https://openalex.org/W2972621596","https://openalex.org/W2987110216","https://openalex.org/W2987322772","https://openalex.org/W3042556523","https://openalex.org/W3133882559","https://openalex.org/W3151922143","https://openalex.org/W3164289800","https://openalex.org/W3177052299","https://openalex.org/W4200634144","https://openalex.org/W4250174831","https://openalex.org/W4252959399","https://openalex.org/W6795488267"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"With":[0,63],"the":[1,4,7,25,30,46,55,64,97,106,113,128,133,149,153,156,169,180],"innovation":[2],"of":[3,9,24,43,51,67,72,115,155],"industrial":[5,10,52],"revolution,":[6],"role":[8],"production":[11],"in":[12,29],"people's":[13],"life":[14,49],"is":[15,36,59,71,159,171],"becoming":[16],"more":[17,19],"and":[18,21,48,132],"important,":[20],"as":[22],"one":[23],"indispensable":[26],"basic":[27],"industries":[28],"industry":[31],"-":[32],"steel,":[33],"its":[34],"demand":[35],"also":[37,60,168],"growing":[38],"rapidly.":[39],"The":[40,118,142],"good":[41],"quality":[42,47],"steel":[44,86],"determines":[45],"cycle":[50],"products,":[53],"but":[54,167],"current":[56],"artificial":[57],"detection":[58,78,88,181],"certain":[61],"limitations.":[62],"rapid":[65],"development":[66],"deep":[68,80],"learning,":[69],"it":[70],"great":[73],"significance":[74],"to":[75,105,111,126,138,175],"combine":[76],"defect":[77,87],"with":[79,148],"learning":[81],"[1-4].":[82],"Therefore,":[83],"an":[84],"improved":[85,98,157],"algorithm":[89,158],"based":[90],"on":[91],"Yolov5":[92],"[5]":[93],"was":[94,103,124],"proposed.":[95],"In":[96],"algorithm,":[99],"attention":[100],"mechanism":[101],"[6]":[102],"added":[104],"convolutional":[107],"network":[108,116],"[7]":[109],"module":[110],"strengthen":[112],"extraction":[114],"features.":[117],"lightweight":[119],"CARAFE":[120],"[8]":[121],"up-sampling":[122,130],"operator":[123],"used":[125],"replace":[127],"original":[129,150],"method,":[131],"extracted":[134],"features":[135],"were":[136],"enlarged":[137],"a":[139],"higher":[140],"level.":[141],"experimental":[143],"results":[144],"show":[145],"that,":[146],"compared":[147],"Yolov5s":[151],"model,":[152],"accuracy":[154],"not":[160],"only":[161],"increased":[162,172],"by":[163],"5.6":[164],"percentage":[165],"points,":[166],"mAP":[170],"from":[173],"77%":[174],"82.6%,":[176],"which":[177],"greatly":[178],"improves":[179],"efficiency.":[182]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
