{"id":"https://openalex.org/W3196514649","doi":"https://doi.org/10.1145/3460426.3463666","title":"A Beneficial Dual Transformation Approach for Deep Learning Networks Used in Steel Surface Defect Detection","display_name":"A Beneficial Dual Transformation Approach for Deep Learning Networks Used in Steel Surface Defect Detection","publication_year":2021,"publication_date":"2021-08-24","ids":{"openalex":"https://openalex.org/W3196514649","doi":"https://doi.org/10.1145/3460426.3463666","mag":"3196514649"},"language":"en","primary_location":{"id":"doi:10.1145/3460426.3463666","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460426.3463666","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 International Conference on Multimedia Retrieval","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/A5006920248","display_name":"Fityanul Akhyar","orcid":"https://orcid.org/0000-0003-3855-4175"},"institutions":[{"id":"https://openalex.org/I99908691","display_name":"Yuan Ze University","ror":"https://ror.org/01fv1ds98","country_code":"TW","type":"education","lineage":["https://openalex.org/I99908691"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Fityanul Akhyar","raw_affiliation_strings":["Yuan Ze University &amp; Telkom University, Taoyuan, Taiwan Roc"],"affiliations":[{"raw_affiliation_string":"Yuan Ze University &amp; Telkom University, Taoyuan, Taiwan Roc","institution_ids":["https://openalex.org/I99908691"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087964642","display_name":"Chih\u2010Yang Lin","orcid":"https://orcid.org/0000-0002-0401-8473"},"institutions":[{"id":"https://openalex.org/I99908691","display_name":"Yuan Ze University","ror":"https://ror.org/01fv1ds98","country_code":"TW","type":"education","lineage":["https://openalex.org/I99908691"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chih-Yang Lin","raw_affiliation_strings":["Yuan Ze University, Taoyuan, Taiwan Roc"],"affiliations":[{"raw_affiliation_string":"Yuan Ze University, Taoyuan, Taiwan Roc","institution_ids":["https://openalex.org/I99908691"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085038389","display_name":"Gugan S. Kathiresan","orcid":"https://orcid.org/0000-0002-1277-3145"},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Gugan S. Kathiresan","raw_affiliation_strings":["Vellore Institute of Technology, Chennai, India"],"affiliations":[{"raw_affiliation_string":"Vellore Institute of Technology, Chennai, India","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5006920248"],"corresponding_institution_ids":["https://openalex.org/I99908691"],"apc_list":null,"apc_paid":null,"fwci":1.1861,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.82380216,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"619","last_page":"622"},"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":0.9998999834060669,"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":0.9998999834060669,"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":0.9980000257492065,"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":0.9944000244140625,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7379904985427856},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6713693141937256},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.6519927978515625},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6155524253845215},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5860188007354736},{"id":"https://openalex.org/keywords/bilinear-interpolation","display_name":"Bilinear interpolation","score":0.5439183712005615},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5429087281227112},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.49480146169662476},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48792168498039246},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45384520292282104},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4199429750442505},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2494717240333557}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7379904985427856},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6713693141937256},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.6519927978515625},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6155524253845215},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5860188007354736},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.5439183712005615},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5429087281227112},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.49480146169662476},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48792168498039246},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45384520292282104},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4199429750442505},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2494717240333557},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3460426.3463666","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460426.3463666","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G5841496633","display_name":null,"funder_award_id":"MOST 107-2221-E-155-048-MY3,MOST 110-2634-F-008-001","funder_id":"https://openalex.org/F4320322795","funder_display_name":"Ministry of Science and Technology, Taiwan"}],"funders":[{"id":"https://openalex.org/F4320322795","display_name":"Ministry of Science and Technology, Taiwan","ror":"https://ror.org/02kv4zf79"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2944303778","https://openalex.org/W2950516690","https://openalex.org/W2954875452","https://openalex.org/W2964241181","https://openalex.org/W2969278648","https://openalex.org/W2969825080","https://openalex.org/W2991593724","https://openalex.org/W3014641072","https://openalex.org/W3034959114","https://openalex.org/W3091070512","https://openalex.org/W3125372245","https://openalex.org/W4245224751"],"related_works":["https://openalex.org/W2367652182","https://openalex.org/W2037074416","https://openalex.org/W2101207231","https://openalex.org/W2741873457","https://openalex.org/W1519931592","https://openalex.org/W2319983895","https://openalex.org/W2142192217","https://openalex.org/W2949096641","https://openalex.org/W2970686063","https://openalex.org/W3034745255"],"abstract_inverted_index":{"Steel":[0],"surface":[1,146],"defect":[2,45],"detection":[3,46,97,140],"represents":[4],"a":[5,40,42],"challenging":[6],"task":[7],"in":[8,53,62,75,104],"real-world":[9],"practical":[10],"object":[11,96],"detection.":[12],"Based":[13],"on":[14],"our":[15],"observations,":[16],"there":[17],"are":[18,64,82],"two":[19],"critical":[20],"problems":[21],"which":[22],"create":[23],"this":[24,38,105],"challenge:":[25],"the":[26,32,54,58,67,73,79,89,115,118,124,129,132,136,139,151],"tiny":[27],"size,":[28],"and":[29,94],"vagueness":[30],"of":[31,117,138,141],"defects.":[33],"To":[34],"solve":[35],"these":[36],"problems,":[37],"study":[39],"proposes":[41],"deep":[43],"learning-based":[44],"system":[47],"that":[48],"uses":[49],"automatic":[50],"dual":[51],"transformation":[52,109],"end-to-end":[55],"network.":[56],"First,":[57],"original":[59],"training":[60],"images":[61],"RGB":[63],"transformed":[65],"into":[66],"HSV":[68],"color":[69,76],"model":[70],"to":[71,87,123,128],"re-arrange":[72],"difference":[74],"distribution.":[77],"Second,":[78],"feature":[80],"maps":[81],"upsampled":[83],"using":[84],"bilinear":[85],"interpolation":[86],"maintain":[88],"smaller":[90],"resolution.":[91],"The":[92],"latest":[93],"state-of-the-art":[95],"model,":[98],"High-Resolution":[99],"Network":[100],"(HRNet)":[101],"is":[102,121],"utilized":[103],"system,":[106],"with":[107],"initial":[108],"performed":[110],"via":[111],"data":[112],"augmentation.":[113],"Afterward,":[114],"output":[116],"backbone":[119],"stage":[120],"applied":[122],"second":[125],"transformation.":[126],"According":[127],"experimental":[130],"results,":[131],"proposed":[133],"approach":[134],"increases":[135],"accuracy":[137],"class":[142],"1":[143],"Severstal":[144],"steel":[145],"defects":[147],"by":[148],"3.6%":[149],"versus":[150],"baseline.":[152]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
