{"id":"https://openalex.org/W3007464067","doi":"https://doi.org/10.1145/3376067.3376113","title":"Automated Detection of Steel Defects via Machine Learning based on Real-Time Semantic Segmentation","display_name":"Automated Detection of Steel Defects via Machine Learning based on Real-Time Semantic Segmentation","publication_year":2019,"publication_date":"2019-12-20","ids":{"openalex":"https://openalex.org/W3007464067","doi":"https://doi.org/10.1145/3376067.3376113","mag":"3007464067"},"language":"en","primary_location":{"id":"doi:10.1145/3376067.3376113","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3376067.3376113","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Conference on Video and Image Processing","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/A5047946128","display_name":"Kun Qian","orcid":"https://orcid.org/0000-0003-2702-9500"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kun Qian","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5047946128"],"corresponding_institution_ids":["https://openalex.org/I47720641"],"apc_list":null,"apc_paid":null,"fwci":2.8703,"has_fulltext":false,"cited_by_count":29,"citation_normalized_percentile":{"value":0.92309692,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"42","last_page":"46"},"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":0.9997000098228455,"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":0.9997000098228455,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.991100013256073,"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"}},{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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":0.8156497478485107},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6529988050460815},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5751937627792358},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5575183033943176},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.554825484752655},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4899980127811432},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4865333139896393},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.44950050115585327},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4247537851333618},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4244523048400879},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.42286789417266846},{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.41688403487205505}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8156497478485107},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6529988050460815},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5751937627792358},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5575183033943176},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.554825484752655},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4899980127811432},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4865333139896393},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.44950050115585327},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4247537851333618},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4244523048400879},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.42286789417266846},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.41688403487205505},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3376067.3376113","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3376067.3376113","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Conference on Video and Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6299999952316284,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1821462560","https://openalex.org/W2092072518","https://openalex.org/W2188776766","https://openalex.org/W2194775991","https://openalex.org/W2331846299","https://openalex.org/W2549139847","https://openalex.org/W2565639579","https://openalex.org/W2901299405","https://openalex.org/W2910628332","https://openalex.org/W2946948417","https://openalex.org/W2963918968","https://openalex.org/W2967098998","https://openalex.org/W4309845474","https://openalex.org/W6687483927","https://openalex.org/W6743731764"],"related_works":["https://openalex.org/W4390516098","https://openalex.org/W4249847449","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W44395729","https://openalex.org/W4205302943","https://openalex.org/W2142795561","https://openalex.org/W2561132942","https://openalex.org/W2765338038","https://openalex.org/W3155418658"],"abstract_inverted_index":{"To":[0],"improve":[1],"automation,":[2],"increase":[3],"efficiency,":[4],"and":[5,58,69,91,126,178,190,206],"maintain":[6],"high":[7],"quality":[8],"in":[9,29,37,44,84,145,152,224],"the":[10,26,30,45,50,141,153,160,202,211,218,225],"production":[11,52],"of":[12,54,80,109,113,134,163,195],"steel,":[13],"applying":[14],"modern":[15],"machine":[16,110,182],"learning":[17,111,183],"techniques":[18],"to":[19,76,94,100],"help":[20],"detect":[21],"steel":[22,31,55,65,81,135,143],"defects":[23,61,83,136],"has":[24,41,170],"been":[25,42],"research":[27],"focus":[28],"industry,":[32],"since":[33],"an":[34,89],"unprecedented":[35],"revolution":[36],"image":[38,85,132],"semantic":[39,115],"segmentation":[40,179],"witnessed":[43],"past":[46],"few":[47],"years.":[48],"In":[49,97],"traditional":[51],"process":[53],"materials,":[56],"localizing":[57],"classifying":[59],"surface":[60,82],"manually":[62],"on":[63,124,201,210],"a":[64,73,107,148,171,193],"sheet":[66],"is":[67,137],"inefficient":[68],"error-prone.":[70],"Therefore,":[71],"it's":[72],"key":[74],"challenge":[75],"achieve":[77,185],"automated":[78],"detection":[79],"pixel":[86],"level,":[87],"leaving":[88],"urgent":[90],"critical":[92],"issue":[93],"be":[95],"addressed.":[96],"this":[98,102],"paper,":[99],"accomplish":[101],"crucial":[103],"task,":[104],"we":[105],"apply":[106],"series":[108],"algorithms":[112,184],"real-time":[114],"segmentation,":[116],"utilizing":[117],"neural":[118,165],"networks":[119,166],"with":[120,167],"encoder-decoder":[121,168],"architectures":[122,169],"based":[123],"Unet":[125],"feature":[127],"pyramid":[128],"network":[129],"(FPN).":[130],"The":[131,156],"dataset":[133],"provided":[138],"by":[139],"Severstal,":[140],"largest":[142],"company":[144],"Russia,":[146],"through":[147],"featured":[149],"code":[150],"competition":[151],"Kaggle":[154,212],"community.":[155],"results":[157],"show":[158],"that":[159],"ensemble":[161],"algorithm":[162],"several":[164],"decent":[172],"performance":[173],"regarding":[174],"both":[175],"time":[176],"cost":[177],"accuracy.":[180],"Our":[181],"dice":[186],"coefficients":[187],"over":[188,196],"0.915":[189],"0.905":[191],"at":[192,217],"speed":[194],"1.5":[197],"images":[198],"per":[199],"second":[200],"public":[203],"test":[204,208],"set":[205,209],"private":[207],"platform,":[213],"respectively,":[214],"which":[215],"locates":[216],"top":[219],"2%":[220],"among":[221],"all":[222],"teams":[223],"competition.":[226]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}