{"id":"https://openalex.org/W3094014653","doi":"https://doi.org/10.3233/atde190094","title":"Deep Learning Based Vision Inspection System for Remanufacturing Application","display_name":"Deep Learning Based Vision Inspection System for Remanufacturing Application","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W3094014653","doi":"https://doi.org/10.3233/atde190094","mag":"3094014653"},"language":"en","primary_location":{"id":"doi:10.3233/atde190094","doi":"https://doi.org/10.3233/atde190094","is_oa":false,"landing_page_url":"https://doi.org/10.3233/atde190094","pdf_url":null,"source":{"id":"https://openalex.org/S4210187496","display_name":"Advances in transdisciplinary engineering","issn_l":"2352-751X","issn":["2352-751X","2352-7528"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"book series"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Transdisciplinary Engineering","raw_type":"book-chapter"},"volume":null,"type":"book-chapter","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://strathprints.strath.ac.uk/74304/1/Nwankpa_etal_ICOR2019_Deep_learning_based_vision_inspection_system.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027476356","display_name":"Chigozie Enyinna Nwankpa","orcid":"https://orcid.org/0000-0003-4966-7669","observed_orcids":["https://orcid.org/0000-0003-4966-7669"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nwankpa Chigozie","raw_affiliation_strings":["Design, Manufacturing And Engineering Management"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Design, Manufacturing And Engineering Management","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018950270","display_name":"Solomon Eze","orcid":"https://orcid.org/0000-0002-4023-0922","observed_orcids":["https://orcid.org/0000-0002-4023-0922"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eze Solomon","raw_affiliation_strings":["Design, Manufacturing And Engineering Management"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Design, Manufacturing And Engineering Management","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025756223","display_name":"Winifred Ijomah","orcid":"https://orcid.org/0000-0002-0652-1486","observed_orcids":["https://orcid.org/0000-0002-0652-1486"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ijomah Winifred","raw_affiliation_strings":["Design, Manufacturing And Engineering Management"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Design, Manufacturing And Engineering Management","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090375849","display_name":"Anthony Gachagan","orcid":"https://orcid.org/0000-0002-9728-4120","observed_orcids":["https://orcid.org/0000-0002-9728-4120"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gachagan Anthony","raw_affiliation_strings":["Electronic And Electrical Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronic And Electrical Engineering","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Marshal Steven","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marshal Steven","raw_affiliation_strings":["Design, Manufacturing And Engineering Management"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Design, Manufacturing And Engineering Management","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9842,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.73500967,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.9994999766349792,"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":0.9994999766349792,"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":9.999999747378752e-05,"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/T12169","display_name":"Non-Destructive Testing Techniques","score":9.999999747378752e-05,"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/remanufacturing","display_name":"remanufacturing","score":0.8899999856948853},{"id":"https://openalex.org/keywords/automated-visual-inspection","display_name":"automated visual inspection","score":0.7369999885559082},{"id":"https://openalex.org/keywords/defect-detection","display_name":"defect detection","score":0.7770000100135803},{"id":"https://openalex.org/keywords/pitting-corrosion","display_name":"pitting corrosion","score":0.6690000295639038},{"id":"https://openalex.org/keywords/surface-abrasion","display_name":"surface abrasion","score":0.9929999709129333},{"id":"https://openalex.org/keywords/cracks","display_name":"cracks","score":0.9290000200271606},{"id":"https://openalex.org/keywords/mild-steel-plates","display_name":"mild steel plates","score":0.8090000152587891},{"id":"https://openalex.org/keywords/deep-convolutional-neural-networks","display_name":"deep convolutional neural networks","score":0.9100000262260437}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7360594272613525},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.695106029510498},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5993990302085876},{"id":"https://openalex.org/C2778738845","wikidata":"https://www.wikidata.org/wiki/Q1521829","display_name":"Remanufacturing","level":2,"score":0.5882605314254761},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5855356454849243},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5516046285629272},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.4872061610221863},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4295233190059662},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.4180629253387451},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41798287630081177},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.4054147005081177},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3722306489944458},{"id":"https://openalex.org/C117671659","wikidata":"https://www.wikidata.org/wiki/Q11049265","display_name":"Manufacturing engineering","level":1,"score":0.15337920188903809},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.13118353486061096},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.08617019653320312}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3233/atde190094","doi":"https://doi.org/10.3233/atde190094","is_oa":false,"landing_page_url":"https://doi.org/10.3233/atde190094","pdf_url":null,"source":{"id":"https://openalex.org/S4210187496","display_name":"Advances in transdisciplinary engineering","issn_l":"2352-751X","issn":["2352-751X","2352-7528"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"book series"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Transdisciplinary Engineering","raw_type":"book-chapter"},{"id":"pmh:oai:strathprints.strath.ac.uk:74304","doi":null,"is_oa":true,"landing_page_url":"https://strathprints.strath.ac.uk/74304/","pdf_url":"https://strathprints.strath.ac.uk/74304/1/Nwankpa_etal_ICOR2019_Deep_learning_based_vision_inspection_system.pdf","source":{"id":"https://openalex.org/S4306402226","display_name":"Strathprints: The University of Strathclyde institutional repository (University of Strathclyde)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I181647926","host_organization_name":"University of Strathclyde","host_organization_lineage":["https://openalex.org/I181647926"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference or Workshop Item"},{"id":"mag:3094014653","doi":null,"is_oa":false,"landing_page_url":"https://pureportal.strath.ac.uk/en/publications/deep-learning-based-vision-inspection-system-for-remanufacturing-","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:strathprints.strath.ac.uk:74304","doi":null,"is_oa":true,"landing_page_url":"https://strathprints.strath.ac.uk/74304/","pdf_url":"https://strathprints.strath.ac.uk/74304/1/Nwankpa_etal_ICOR2019_Deep_learning_based_vision_inspection_system.pdf","source":{"id":"https://openalex.org/S4306402226","display_name":"Strathprints: The University of Strathclyde institutional repository (University of Strathclyde)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I181647926","host_organization_name":"University of Strathclyde","host_organization_lineage":["https://openalex.org/I181647926"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference or Workshop Item"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5378999710083008}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3094014653.pdf","grobid_xml":"https://content.openalex.org/works/W3094014653.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W1493858944","https://openalex.org/W1834816129","https://openalex.org/W1910657905","https://openalex.org/W1969977550","https://openalex.org/W1987216676","https://openalex.org/W1994069847","https://openalex.org/W2001507642","https://openalex.org/W2020307908","https://openalex.org/W2027289052","https://openalex.org/W2029123070","https://openalex.org/W2039678421","https://openalex.org/W2055789146","https://openalex.org/W2062457789","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2117731089","https://openalex.org/W2124386111","https://openalex.org/W2131438770","https://openalex.org/W2145023731","https://openalex.org/W2149933564","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2163775665","https://openalex.org/W2394667775","https://openalex.org/W2586704709","https://openalex.org/W2589306531","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W3089906958","https://openalex.org/W4319073328","https://openalex.org/W4401167272","https://openalex.org/W4393304419","https://openalex.org/W4415103235","https://openalex.org/W3124804470","https://openalex.org/W3147809269","https://openalex.org/W3203106191","https://openalex.org/W4406379189","https://openalex.org/W2991481387"],"abstract_inverted_index":{"Deep":[0,29,49,92,128,254,282],"Learning":[1,30,50,93,255,283],"has":[2,232],"emerged":[3],"as":[4,235,274],"a":[5,10,58,122,171,204,236,275],"state-of-the-art":[6],"learning":[7],"technique":[8,102],"across":[9],"wide":[11],"range":[12],"of":[13,37,136,176,181,211,281],"applications,":[14],"including":[15],"image":[16],"recognition,":[17],"localisation,":[18],"natural":[19],"language":[20],"processing,":[21],"prediction":[22],"and":[23,116,142,168,199,217,247],"forecasting":[24],"systems.":[25],"With":[26],"significant":[27],"applicability,":[28],"is":[31,44,57,64],"continually":[32],"seeking":[33],"other":[34],"new":[35],"fronts":[36],"applications":[38,280],"for":[39,95,133,154,215,221,245,278],"these":[40,147,271],"techniques.":[41],"This":[42,83,119,249,265],"research":[43,84,266],"the":[45,71,104,187,212,218,225,229,241,258],"first":[46],"to":[47,52,106,193,260,285],"apply":[48],"algorithm":[51,94],"inspection":[53,98,125,262],"in":[54,61,70,80,110,224,263],"remanufacturing.":[55,264,286],"Inspection":[56],"key":[59],"process":[60,73],"remanufacturing,":[62],"which":[63],"currently":[65],"an":[66,87],"expensive":[67],"manual":[68],"operation":[69],"remanufacturing":[72,226],"that":[74,186,238,253],"depends":[75],"on":[76,91,127,196,203],"human":[77,108],"operator":[78],"expertise,":[79],"most":[81],"cases.":[82],"further":[85],"proposes":[86],"automation":[88],"framework":[89],"based":[90],"automating":[96],"this":[97,155,182],"process.":[99],"The":[100,151,179],"proposed":[101],"offers":[103,267],"potential":[105,234,259],"eliminate":[107],"factors":[109],"inspection,":[111,228],"save":[112],"cost,":[113],"increase":[114],"throughput":[115],"improve":[117],"precision.":[118],"paper":[120],"presents":[121],"novel":[123],"vision-based":[124],"system":[126],"Convolution":[129],"Neural":[130],"Network":[131],"(DCNN)":[132],"three":[134],"types":[135],"defects,":[137],"namely":[138],"pitting,":[139],"surface":[140,148],"abrasion":[141],"cracks":[143],"by":[144,208],"distinguishing":[145],"between":[146],"defected":[149],"parts.":[150],"materials":[152],"used":[153],"feasibility":[156],"study":[157,184,251],"were":[158],"100cm":[159],"x":[160],"150cm":[161],"mild":[162],"steel":[163],"plate":[164],"material,":[165],"purchased":[166],"locally,":[167],"captured":[169],"using":[170,209],"web":[172],"webcam":[173],"USB":[174],"camera":[175],"0.3":[177],"megapixels.":[178],"performance":[180],"preliminary":[183,250],"indicates":[185],"DCNN":[188,230],"can":[189],"classify":[190],"with":[191],"up":[192],"100%":[194],"accuracy":[195,202,246],"validation":[197],"data":[198],"above":[200],"96%":[201],"live":[205],"video":[206],"feed,":[207],"80%":[210],"sample":[213],"dataset":[214],"training":[216],"remaining":[219],"20%":[220],"testing.":[222],"Therefore,":[223],"parts":[227],"approach":[231],"high":[233],"method":[237],"could":[239],"surpass":[240],"current":[242],"technologies,":[243],"especially":[244],"speed.":[248],"demonstrates":[252],"techniques":[256],"have":[257],"revolutionise":[261],"valuable":[268],"insight":[269],"into":[270],"opportunities,":[272],"serving":[273],"starting":[276],"point":[277],"future":[279],"algorithms":[284]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-11-13T13:42:25.865203","created_date":"2020-10-29T00:00:00"}