{"id":"https://openalex.org/W3108111683","doi":"https://doi.org/10.1109/access.2020.3039356","title":"A New Backbone Network for Instance Segmentation: Application on a Semiconductor Process Inspection","display_name":"A New Backbone Network for Instance Segmentation: Application on a Semiconductor Process Inspection","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3108111683","doi":"https://doi.org/10.1109/access.2020.3039356","mag":"3108111683"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3039356","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3039356","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09264148.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/ielx7/6287639/6514899/09264148.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103097778","display_name":"Junghee Han","orcid":"https://orcid.org/0000-0003-2592-4341"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junghee Han","raw_affiliation_strings":["Graduate School of Convergence Science and Technology (GSCST), Seoul National University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-2592-4341","affiliations":[{"raw_affiliation_string":"Graduate School of Convergence Science and Technology (GSCST), Seoul National University, Seoul, South Korea","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101780440","display_name":"Seongsoo Hong","orcid":"https://orcid.org/0009-0004-7668-5613"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seongsoo Hong","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"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":0.1757,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.61014294,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"8","issue":null,"first_page":"218110","last_page":"218121"},"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":0.9995999932289124,"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":0.9995999932289124,"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":0.9994000196456909,"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":0.9955999851226807,"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":0.8381879329681396},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6456897854804993},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.612734317779541},{"id":"https://openalex.org/keywords/concatenation","display_name":"Concatenation (mathematics)","score":0.5957183241844177},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5850360989570618},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5389009714126587},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5290122628211975},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5268465280532837},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47789132595062256},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4753655791282654},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43154746294021606},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.41852980852127075},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.41844069957733154},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38560760021209717}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8381879329681396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6456897854804993},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.612734317779541},{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.5957183241844177},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5850360989570618},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5389009714126587},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5290122628211975},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5268465280532837},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47789132595062256},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4753655791282654},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43154746294021606},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.41852980852127075},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.41844069957733154},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38560760021209717},{"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3039356","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3039356","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09264148.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:cbd4351d7faa4fbea0e038bedb6a8290","is_oa":true,"landing_page_url":"https://doaj.org/article/cbd4351d7faa4fbea0e038bedb6a8290","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 8, Pp 218110-218121 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3039356","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3039356","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09264148.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":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3108111683.pdf","grobid_xml":"https://content.openalex.org/works/W3108111683.grobid-xml"},"referenced_works_count":109,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1731081199","https://openalex.org/W1836465849","https://openalex.org/W1923697677","https://openalex.org/W1948751323","https://openalex.org/W1963851088","https://openalex.org/W1971227160","https://openalex.org/W2010783536","https://openalex.org/W2011520966","https://openalex.org/W2046148089","https://openalex.org/W2078417145","https://openalex.org/W2086243709","https://openalex.org/W2097117768","https://openalex.org/W2102605133","https://openalex.org/W2104094955","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2114833239","https://openalex.org/W2115403315","https://openalex.org/W2133076926","https://openalex.org/W2134726061","https://openalex.org/W2144509369","https://openalex.org/W2156387975","https://openalex.org/W2165698076","https://openalex.org/W2271840356","https://openalex.org/W2274287116","https://openalex.org/W2279236905","https://openalex.org/W2288122362","https://openalex.org/W2298257252","https://openalex.org/W2302255633","https://openalex.org/W2395611524","https://openalex.org/W2515655118","https://openalex.org/W2549139847","https://openalex.org/W2557728737","https://openalex.org/W2559597482","https://openalex.org/W2613718673","https://openalex.org/W2618530766","https://openalex.org/W2725513608","https://openalex.org/W2746808752","https://openalex.org/W2756827438","https://openalex.org/W2768489488","https://openalex.org/W2770456481","https://openalex.org/W2790607928","https://openalex.org/W2805906705","https://openalex.org/W2809190443","https://openalex.org/W2811169353","https://openalex.org/W2884412841","https://openalex.org/W2884561390","https://openalex.org/W2890715498","https://openalex.org/W2891004411","https://openalex.org/W2900595477","https://openalex.org/W2905017682","https://openalex.org/W2913414428","https://openalex.org/W2914663144","https://openalex.org/W2949117887","https://openalex.org/W2950565945","https://openalex.org/W2950800384","https://openalex.org/W2950954173","https://openalex.org/W2954996726","https://openalex.org/W2962917547","https://openalex.org/W2962992847","https://openalex.org/W2963016543","https://openalex.org/W2963037989","https://openalex.org/W2963150001","https://openalex.org/W2963150697","https://openalex.org/W2963342610","https://openalex.org/W2963351448","https://openalex.org/W2963446712","https://openalex.org/W2963459241","https://openalex.org/W2963840672","https://openalex.org/W2964080601","https://openalex.org/W2964241181","https://openalex.org/W2964350391","https://openalex.org/W2966140116","https://openalex.org/W2967330081","https://openalex.org/W2988916019","https://openalex.org/W3106250896","https://openalex.org/W4239072543","https://openalex.org/W6637373629","https://openalex.org/W6637618735","https://openalex.org/W6638667902","https://openalex.org/W6640295612","https://openalex.org/W6640759395","https://openalex.org/W6674914833","https://openalex.org/W6675026286","https://openalex.org/W6682889407","https://openalex.org/W6694260854","https://openalex.org/W6694517276","https://openalex.org/W6696085341","https://openalex.org/W6696141924","https://openalex.org/W6714138976","https://openalex.org/W6725739302","https://openalex.org/W6726347659","https://openalex.org/W6729342207","https://openalex.org/W6740164494","https://openalex.org/W6742813915","https://openalex.org/W6745718170","https://openalex.org/W6746085279","https://openalex.org/W6746348132","https://openalex.org/W6746975906","https://openalex.org/W6749810415","https://openalex.org/W6752520475","https://openalex.org/W6754507053","https://openalex.org/W6754632766","https://openalex.org/W6754874917","https://openalex.org/W6758447463","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2373577936","https://openalex.org/W3095575180","https://openalex.org/W2389596151","https://openalex.org/W4221148444","https://openalex.org/W3183901164","https://openalex.org/W2951211570","https://openalex.org/W3135818718","https://openalex.org/W4290188444","https://openalex.org/W2253429366","https://openalex.org/W3127975138"],"abstract_inverted_index":{"In":[0,121],"this":[1,151],"paper,":[2],"we":[3],"propose":[4],"Instance":[5],"Segmentation":[6],"Detector":[7],"(ISD)":[8],"to":[9,109],"extract":[10,110],"the":[11,15,23,65,69,72,104,111,125,129,149,154,159,165,180],"enhanced":[12,113],"feature-maps":[13,73],"under":[14],"situations":[16],"where":[17],"training":[18],"dataset":[19,99],"is":[20,34,82,95,128],"limited":[21],"in":[22,62,75,164],"specific":[24,105],"industry":[25],"domain":[26],"such":[27],"as":[28,36],"semiconductor":[29,168],"photo":[30],"lithography":[31],"inspection.":[32],"ISD":[33,49,63,81,94,171,187,201],"used":[35],"a":[37,132],"new":[38],"backbone":[39],"network":[40],"of":[41,51,71,103,116,124],"state-of-the-art":[42,177],"Mask":[43],"R-CNN":[44],"framework":[45],"for":[46,135,141],"instance":[47],"segmentation.":[48],"consists":[50],"four":[52,56],"dense":[53,60],"blocks":[54],"and":[55,68,140],"transition":[57],"layers.":[58],"Each":[59],"block":[61],"has":[64],"shortcut":[66],"connection":[67],"concatenation":[70],"produced":[74],"layer":[76],"with":[77,97,209],"dynamic":[78],"growth":[79],"rate.":[80],"trained":[83,96],"from":[84,158],"scratch":[85],"without":[86],"using":[87,215],"recently":[88],"approached":[89],"transfer":[90],"learning":[91],"method.":[92],"Additionally,":[93],"image":[98,107,156],"pre-processed":[100],"by":[101],"means":[102],"designed":[106],"filter":[108],"better":[112,174,205],"feature":[114],"map":[115],"Convolutional":[117],"Neural":[118],"Network":[119],"(CNN).":[120],"ISD,":[122],"one":[123],"key":[126],"principles":[127],"compactness,":[130],"plays":[131],"critical":[133],"role":[134],"addressing":[136],"real":[137,155],"time":[138],"problem":[139],"application":[142],"on":[143],"resource":[144],"bounded":[145],"devices.":[146],"To":[147],"validate":[148],"model,":[150],"paper":[152],"uses":[153],"collected":[157],"computer":[160],"vision":[161],"system":[162],"embedded":[163],"currently":[166],"operating":[167],"manufacturing":[169],"equipment.":[170],"achieves":[172],"consistently":[173],"results":[175,206],"than":[176,207],"methods":[178],"at":[179],"standard":[181],"mean":[182],"average":[183],"precision.":[184],"Specifically,":[185],"our":[186],"outperforms":[188],"baseline":[189],"method":[190],"DenseNet,":[191],"while":[192],"requiring":[193],"only":[194,210],"1/4":[195],"parameters.":[196],"We":[197],"also":[198],"observe":[199],"that":[200],"can":[202],"achieve":[203],"comparable":[204],"ResNet,":[208],"much":[211],"smaller":[212],"1/268":[213],"parameters,":[214],"no":[216],"extra":[217],"data":[218],"or":[219],"pre-trained":[220],"models.":[221]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
