{"id":"https://openalex.org/W3116796287","doi":"https://doi.org/10.1109/access.2020.3047221","title":"A Two-Stage Multiscale Residual Attention Network for Light Guide Plate Defect Detection","display_name":"A Two-Stage Multiscale Residual Attention Network for Light Guide Plate Defect Detection","publication_year":2020,"publication_date":"2020-12-24","ids":{"openalex":"https://openalex.org/W3116796287","doi":"https://doi.org/10.1109/access.2020.3047221","mag":"3116796287"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3047221","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3047221","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09306797.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09306797.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024614968","display_name":"Zhaopan Li","orcid":"https://orcid.org/0000-0002-4386-4197"},"institutions":[{"id":"https://openalex.org/I1328775524","display_name":"Zhejiang Sci-Tech University","ror":"https://ror.org/03893we55","country_code":"CN","type":"education","lineage":["https://openalex.org/I1328775524"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaopan Li","raw_affiliation_strings":["Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-4386-4197","affiliations":[{"raw_affiliation_string":"Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou, China","institution_ids":["https://openalex.org/I1328775524"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112595617","display_name":"Junfengs Li","orcid":null},"institutions":[{"id":"https://openalex.org/I1328775524","display_name":"Zhejiang Sci-Tech University","ror":"https://ror.org/03893we55","country_code":"CN","type":"education","lineage":["https://openalex.org/I1328775524"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junfengs Li","raw_affiliation_strings":["Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-1207-3317","affiliations":[{"raw_affiliation_string":"Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou, China","institution_ids":["https://openalex.org/I1328775524"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108443951","display_name":"Wenzhan Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I75059550","display_name":"Zhejiang Gongshang University","ror":"https://ror.org/0569mkk41","country_code":"CN","type":"education","lineage":["https://openalex.org/I75059550"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenzhan Dai","raw_affiliation_strings":["School of Information and Electronic Engineering, Zhejiang GongShang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Electronic Engineering, Zhejiang GongShang University, Hangzhou, China","institution_ids":["https://openalex.org/I75059550"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.9863,"has_fulltext":true,"cited_by_count":30,"citation_normalized_percentile":{"value":0.92219829,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"9","issue":null,"first_page":"2780","last_page":"2792"},"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":0.9979000091552734,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9934999942779541,"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/subnet","display_name":"Subnet","score":0.9510960578918457},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7782098054885864},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.707714319229126},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6680194139480591},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6490830779075623},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.49797677993774414},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48877665400505066},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.47142213582992554},{"id":"https://openalex.org/keywords/stage","display_name":"Stage (stratigraphy)","score":0.44497549533843994},{"id":"https://openalex.org/keywords/brightness","display_name":"Brightness","score":0.4208641052246094},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.12894508242607117}],"concepts":[{"id":"https://openalex.org/C21099817","wikidata":"https://www.wikidata.org/wiki/Q7631721","display_name":"Subnet","level":2,"score":0.9510960578918457},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7782098054885864},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.707714319229126},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6680194139480591},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6490830779075623},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.49797677993774414},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48877665400505066},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47142213582992554},{"id":"https://openalex.org/C146357865","wikidata":"https://www.wikidata.org/wiki/Q1123245","display_name":"Stage (stratigraphy)","level":2,"score":0.44497549533843994},{"id":"https://openalex.org/C125245961","wikidata":"https://www.wikidata.org/wiki/Q221656","display_name":"Brightness","level":2,"score":0.4208641052246094},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.12894508242607117},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3047221","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3047221","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09306797.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:714d5464a13b4863aa085faf2b33f4ee","is_oa":true,"landing_page_url":"https://doaj.org/article/714d5464a13b4863aa085faf2b33f4ee","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 2780-2792 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3047221","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3047221","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09306797.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8718015563","display_name":null,"funder_award_id":"61374022","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3116796287.pdf","grobid_xml":"https://content.openalex.org/works/W3116796287.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W234388709","https://openalex.org/W1522301498","https://openalex.org/W1836465849","https://openalex.org/W1901129140","https://openalex.org/W2194775991","https://openalex.org/W2412782625","https://openalex.org/W2418691539","https://openalex.org/W2512995176","https://openalex.org/W2560023338","https://openalex.org/W2589306531","https://openalex.org/W2598457882","https://openalex.org/W2598666589","https://openalex.org/W2752782242","https://openalex.org/W2800240267","https://openalex.org/W2805484002","https://openalex.org/W2884585870","https://openalex.org/W2922017402","https://openalex.org/W2922509574","https://openalex.org/W2923486253","https://openalex.org/W2928165649","https://openalex.org/W2955058313","https://openalex.org/W2963037463","https://openalex.org/W2963043051","https://openalex.org/W2963420686","https://openalex.org/W2963495494","https://openalex.org/W2963542991","https://openalex.org/W2963843116","https://openalex.org/W2969890541","https://openalex.org/W2988396473","https://openalex.org/W3036289652","https://openalex.org/W3104156061","https://openalex.org/W4297810817","https://openalex.org/W6629368666","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6639824700","https://openalex.org/W6753038380","https://openalex.org/W6753412334","https://openalex.org/W6766029800"],"related_works":["https://openalex.org/W2102539527","https://openalex.org/W2131631951","https://openalex.org/W2356206668","https://openalex.org/W2130707537","https://openalex.org/W1977409556","https://openalex.org/W2361602549","https://openalex.org/W4362683600","https://openalex.org/W2105155969","https://openalex.org/W3209312100","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Aiming":[0],"at":[1],"the":[2,7,13,19,29,57,74,78,86,99,105,116,119,157,165,174,189,199,209,227,233],"difference":[3],"in":[4,177,182],"density":[5],"between":[6],"light":[8,14],"guide":[9,15],"point":[10],"distribution":[11],"of":[12,25,31,35,69,107,115,128,141,159,167,191,195],"plate":[16],"(LGP)":[17],"images,":[18],"different":[20],"size,":[21],"shape":[22],"and":[23,28,77,163,186,206],"brightness":[24],"LGP":[26,53,100],"defects,":[27],"limitation":[30,190],"a":[32,41,60,89,125,132,139,192,218],"small":[33,126,193],"number":[34,127,194],"defect":[36,54,220],"samples,":[37,129],"our":[38],"paper":[39,179],"proposes":[40],"two-stage":[42],"multiscale":[43,70,168],"residual":[44,160],"attention":[45,161],"network":[46,117],"based":[47],"on":[48,146,208,232],"\u201csegmentation":[49],"+":[50],"decision\u201d":[51],"for":[52,135],"detection.":[55],"In":[56,85,197],"first":[58],"stage,":[59,88],"segmentation":[61,111],"subnet":[62,91],"is":[63,92,102,131,223],"constructed":[64,93],"to":[65,94,121,225],"achieve":[66,95],"accurate":[67,96],"location":[68],"defects":[71],"by":[72,110],"using":[73,124],"U-shaped":[75],"structure":[76],"designed":[79],"Multiscale":[80],"Residual":[81],"Attention":[82],"Unit":[83],"(MRAU).":[84],"second":[87],"decision":[90,97],"whether":[98],"image":[101,215],"defective":[103],"under":[104,188],"guidance":[106],"features":[108],"extracted":[109],"subnet.":[112],"The":[113],"design":[114],"enables":[118],"model":[120],"be":[122],"trained":[123],"which":[130],"significant":[133],"requirement":[134],"practical":[136],"applications.":[137],"Finally,":[138],"lot":[140],"experimental":[142],"studies":[143],"were":[144],"conducted":[145],"two":[147,210],"datasets,":[148],"including":[149],"compared":[150],"with":[151,213],"related":[152],"deep":[153],"learning":[154],"methods,":[155],"proving":[156,164],"effectiveness":[158,166],"mechanism":[162],"mechanism.":[169],"Experimental":[170],"results":[171],"show":[172],"that":[173],"detection":[175,184,200,221],"method":[176],"this":[178],"has":[180],"advantages":[181],"both":[183],"accuracy":[185],"universality":[187],"samples.":[196],"particular,":[198],"indicator":[201],"F1-Score":[202],"respectively":[203],"reached":[204],"99.85%":[205],"95.46%":[207],"datasets.":[211],"Combined":[212],"an":[214],"acquisition":[216],"platform,":[217],"surface":[219],"system":[222],"implemented":[224],"realize":[226],"real-time":[228],"production":[229,234],"quality":[230],"inspection":[231],"lines.":[235]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}