{"id":"https://openalex.org/W4292387701","doi":"https://doi.org/10.1109/civemsa53371.2022.9853682","title":"Segmentation of Drilled Holes in Textured Wood Panels using Deep Learning Framework","display_name":"Segmentation of Drilled Holes in Textured Wood Panels using Deep Learning Framework","publication_year":2022,"publication_date":"2022-06-15","ids":{"openalex":"https://openalex.org/W4292387701","doi":"https://doi.org/10.1109/civemsa53371.2022.9853682"},"language":"en","primary_location":{"id":"doi:10.1109/civemsa53371.2022.9853682","is_oa":false,"landing_page_url":"https://doi.org/10.1109/civemsa53371.2022.9853682","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 9th International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","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/A5015597603","display_name":"Muneer M. Al-Zu'bi","orcid":"https://orcid.org/0000-0002-4792-7386"},"institutions":[{"id":"https://openalex.org/I186903577","display_name":"University of Luxembourg","ror":"https://ror.org/036x5ad56","country_code":"LU","type":"education","lineage":["https://openalex.org/I186903577"]}],"countries":["LU"],"is_corresponding":true,"raw_author_name":"Muneer Al-Zubi","raw_affiliation_strings":["University of Luxembourg,Department of Engineering,Luxembourg,Luxembourg,L-1359"],"affiliations":[{"raw_affiliation_string":"University of Luxembourg,Department of Engineering,Luxembourg,Luxembourg,L-1359","institution_ids":["https://openalex.org/I186903577"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068617396","display_name":"Peter Plapper","orcid":"https://orcid.org/0000-0003-3507-1397"},"institutions":[{"id":"https://openalex.org/I186903577","display_name":"University of Luxembourg","ror":"https://ror.org/036x5ad56","country_code":"LU","type":"education","lineage":["https://openalex.org/I186903577"]}],"countries":["LU"],"is_corresponding":false,"raw_author_name":"Peter Plapper","raw_affiliation_strings":["University of Luxembourg,Department of Engineering,Luxembourg,Luxembourg,L-1359"],"affiliations":[{"raw_affiliation_string":"University of Luxembourg,Department of Engineering,Luxembourg,Luxembourg,L-1359","institution_ids":["https://openalex.org/I186903577"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5015597603"],"corresponding_institution_ids":["https://openalex.org/I186903577"],"apc_list":null,"apc_paid":null,"fwci":0.1334,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.51987122,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"28","issue":null,"first_page":"1","last_page":"4"},"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9979000091552734,"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/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/segmentation","display_name":"Segmentation","score":0.7234106063842773},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7211371660232544},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6279826164245605},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5446060299873352},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5206941366195679},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5163532495498657},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5103289484977722},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.49052995443344116},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4357934296131134},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4323428273200989},{"id":"https://openalex.org/keywords/drilling","display_name":"Drilling","score":0.42594262957572937},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4070596992969513},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.35140538215637207},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.23823952674865723},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.10836425423622131}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7234106063842773},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7211371660232544},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6279826164245605},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5446060299873352},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5206941366195679},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5163532495498657},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5103289484977722},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.49052995443344116},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4357934296131134},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4323428273200989},{"id":"https://openalex.org/C25197100","wikidata":"https://www.wikidata.org/wiki/Q890886","display_name":"Drilling","level":2,"score":0.42594262957572937},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4070596992969513},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35140538215637207},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.23823952674865723},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.10836425423622131},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/civemsa53371.2022.9853682","is_oa":false,"landing_page_url":"https://doi.org/10.1109/civemsa53371.2022.9853682","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 9th International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6000000238418579}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2111123943","https://openalex.org/W2133059825","https://openalex.org/W2145023731","https://openalex.org/W2194775991","https://openalex.org/W2614270103","https://openalex.org/W2807567209","https://openalex.org/W2945974966","https://openalex.org/W2963150697","https://openalex.org/W2995450656","https://openalex.org/W3006562728","https://openalex.org/W3033682709","https://openalex.org/W3124372372","https://openalex.org/W3164521586","https://openalex.org/W3202477879","https://openalex.org/W4241366218","https://openalex.org/W6620707391","https://openalex.org/W6762255807"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2393925373","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W2964954556","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Automated":[0],"vision-based":[1],"detection":[2,15,45,72,97,136,158],"and":[3,42,62,98,114,135,144,159,188],"inspection":[4],"systems":[5],"become":[6],"a":[7,56,66,142],"key":[8],"process":[9,41,73,161],"in":[10,20,29,104,141,169,178],"industry":[11],"4.0.":[12],"Automatic":[13],"precise":[14],"of":[16,38,46,59,74,100,107,180,197],"the":[17,30,36,39,44,47,70,75,84,96,101,125,138,151,157,163,170,175,181,185,189,193,198],"drilled":[18,76,102,115,126,139,164,199],"holes":[19,103,140,165],"various":[21,111],"textured":[22],"wood":[23,51,108,127],"panels":[24,52,109],"is":[25,122],"an":[26],"essential":[27],"component":[28],"automatic":[31],"assembly":[32,40],"lines":[33],"to":[34,69,191],"ensure":[35],"success":[37],"for":[43,95,124,137,166],"drilling":[48],"defects.":[49],"The":[50,147],"are":[53],"produced":[54],"with":[55,110],"wide":[57],"variety":[58],"complex":[60],"textures":[61],"colors.":[63],"This":[64],"brings":[65],"big":[67],"challenge":[68],"vision":[71],"hole":[77,116],"panels.":[78,128],"In":[79],"this":[80],"paper,":[81],"we":[82],"apply":[83],"deep":[85],"learning":[86],"mask":[87],"region-based":[88],"convolution":[89],"neural":[90],"network":[91],"(Mask":[92],"R-CNN)":[93],"framework":[94],"segmentation":[99,134,160,195],"synthetic":[105,119],"images":[106,168],"textures,":[112],"colors,":[113],"patterns.":[117],"A":[118],"image":[120],"dataset":[121],"generated":[123],"Mask":[129],"R-CNN":[130],"provides":[131],"simultaneous":[132],"instance":[133,194],"flexible":[143],"fast":[145],"manner.":[146],"results":[148],"show":[149],"that":[150],"trained":[152],"model":[153,176],"can":[154],"accurately":[155],"perform":[156],"on":[162],"most":[167],"test":[171],"dataset.":[172],"We":[173],"evaluate":[174],"performance":[177],"terms":[179],"mean":[182],"average":[183],"precision,":[184],"inference":[186],"time,":[187],"ability":[190],"extract":[192],"masks":[196],"holes.":[200]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
