{"id":"https://openalex.org/W3186160365","doi":"https://doi.org/10.1109/siu53274.2021.9477867","title":"Deep Learning-based Semantic Segmentation for Crack Detection on Marbles","display_name":"Deep Learning-based Semantic Segmentation for Crack Detection on Marbles","publication_year":2021,"publication_date":"2021-06-09","ids":{"openalex":"https://openalex.org/W3186160365","doi":"https://doi.org/10.1109/siu53274.2021.9477867","mag":"3186160365"},"language":"en","primary_location":{"id":"doi:10.1109/siu53274.2021.9477867","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siu53274.2021.9477867","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","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/A5037054042","display_name":"\u015eahin Alp Akosman","orcid":"https://orcid.org/0000-0002-0261-7800"},"institutions":[{"id":"https://openalex.org/I250383648","display_name":"Izmir K\u00e2tip \u00c7elebi University","ror":"https://ror.org/024nx4843","country_code":"TR","type":"education","lineage":["https://openalex.org/I250383648"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"\u015eahin Alp Akosman","raw_affiliation_strings":["Izmir Katip Celebi University,Department of Electrical and Electronics Engineering,Izmir,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Izmir Katip Celebi University,Department of Electrical and Electronics Engineering,Izmir,Turkey","institution_ids":["https://openalex.org/I250383648"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022126782","display_name":"Mert \u00d6ktem","orcid":null},"institutions":[{"id":"https://openalex.org/I250383648","display_name":"Izmir K\u00e2tip \u00c7elebi University","ror":"https://ror.org/024nx4843","country_code":"TR","type":"education","lineage":["https://openalex.org/I250383648"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Mert \u00d6ktem","raw_affiliation_strings":["Izmir Katip Celebi University,Department of Electrical and Electronics Engineering,Izmir,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Izmir Katip Celebi University,Department of Electrical and Electronics Engineering,Izmir,Turkey","institution_ids":["https://openalex.org/I250383648"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084137589","display_name":"\u00d6zge Taylan MORAL","orcid":"https://orcid.org/0000-0003-0482-267X"},"institutions":[{"id":"https://openalex.org/I250383648","display_name":"Izmir K\u00e2tip \u00c7elebi University","ror":"https://ror.org/024nx4843","country_code":"TR","type":"education","lineage":["https://openalex.org/I250383648"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"\u00d6zge Taylan Moral","raw_affiliation_strings":["Izmir Katip Celebi University,Department of Electrical and Electronics Engineering,Izmir,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Izmir Katip Celebi University,Department of Electrical and Electronics Engineering,Izmir,Turkey","institution_ids":["https://openalex.org/I250383648"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063512115","display_name":"Volkan K\u0131l\u0131\u00e7","orcid":"https://orcid.org/0000-0002-3164-1981"},"institutions":[{"id":"https://openalex.org/I250383648","display_name":"Izmir K\u00e2tip \u00c7elebi University","ror":"https://ror.org/024nx4843","country_code":"TR","type":"education","lineage":["https://openalex.org/I250383648"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Volkan K\u0131l\u0131\u00e7","raw_affiliation_strings":["Izmir Katip Celebi University,Department of Electrical and Electronics Engineering,Izmir,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Izmir Katip Celebi University,Department of Electrical and Electronics Engineering,Izmir,Turkey","institution_ids":["https://openalex.org/I250383648"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8116,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.70081377,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9998999834060669,"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/T12482","display_name":"Tunneling and Rock Mechanics","score":0.9914000034332275,"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/T10161","display_name":"Rock Mechanics and Modeling","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7968913912773132},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7414575815200806},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7320865392684937},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6756091117858887},{"id":"https://openalex.org/keywords/fracture","display_name":"Fracture (geology)","score":0.5077074766159058},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4942803382873535},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.47152456641197205},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4463005065917969},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4431172311306},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36987948417663574},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.19274228811264038}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7968913912773132},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7414575815200806},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7320865392684937},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6756091117858887},{"id":"https://openalex.org/C43369102","wikidata":"https://www.wikidata.org/wiki/Q2307625","display_name":"Fracture (geology)","level":2,"score":0.5077074766159058},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4942803382873535},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.47152456641197205},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4463005065917969},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4431172311306},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36987948417663574},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.19274228811264038},{"id":"https://openalex.org/C187320778","wikidata":"https://www.wikidata.org/wiki/Q1349130","display_name":"Geotechnical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/siu53274.2021.9477867","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siu53274.2021.9477867","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","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":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2024839283","https://openalex.org/W2093303892","https://openalex.org/W2586750457","https://openalex.org/W2618530766","https://openalex.org/W2754487565","https://openalex.org/W2800346298","https://openalex.org/W2810123099","https://openalex.org/W2848846851","https://openalex.org/W2897598966","https://openalex.org/W2920633487","https://openalex.org/W2963908722","https://openalex.org/W2970209410","https://openalex.org/W3013859038","https://openalex.org/W3016245448","https://openalex.org/W3082043672","https://openalex.org/W3088532819","https://openalex.org/W3097812709","https://openalex.org/W3124942917","https://openalex.org/W3193347270"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W4315434538","https://openalex.org/W1522196789"],"abstract_inverted_index":{"The":[0,19,79],"demands":[1],"for":[2],"the":[3,14,53,58,64,101,105,108,111,126,140,149,171],"improvement":[4],"of":[5,17,21,33,104,107],"production":[6,172],"cost":[7],"and":[8,24,43,77,91,121,128,136,143,166],"quality":[9],"have":[10],"been":[11,82,153],"increased":[12],"with":[13,47,63,115,139,155],"widespread":[15],"application":[16],"marble.":[18],"problem":[20],"detecting":[22],"fractures":[23,76],"cracks":[25],"on":[26,170],"marble":[27,97],"has":[28,81,152],"attracted":[29],"an":[30,39],"increasing":[31],"amount":[32],"attention":[34],"recently.":[35],"In":[36,52],"this":[37],"study,":[38],"artificial":[40],"intelligence-based":[41],"fracture":[42,90],"crack":[44,92,168],"detection":[45,169],"system":[46,151],"high":[48],"accuracy":[49],"is":[50,161],"proposed.":[51],"proposed":[54,150],"system,":[55],"images":[56,88,98],"in":[57,72],"dataset":[59,80,109],"were":[60,132],"first":[61],"trained":[62],"convolutional":[65,118],"neural":[66,119],"network-based":[67],"semantic":[68],"segmentation":[69],"model,":[70],"DeepLabv3+,":[71],"order":[73],"to":[74,100,110,163],"detect":[75],"cracks.":[78],"augmented":[83],"by":[84],"adding":[85],"different":[86,117,123],"ground":[87],"containing":[89],"surfaces":[93],"as":[94,96,134],"well":[95],"due":[99],"positive":[102],"contribution":[103],"size":[106],"accuracy.":[112],"After":[113],"training":[114],"5":[116],"networks":[120],"3":[122],"optimization":[124],"algorithms,":[125],"mIoU":[127,130],"weighted":[129],"values":[131],"achieved":[133],"0.672":[135],"0.967,":[137],"respectively,":[138],"RMSprop":[141],"algorithm":[142],"ResNet-50":[144],"architecture.":[145],"Unlike":[146],"similar":[147],"studies,":[148],"integrated":[154],"our":[156],"custom-designed":[157],"interface.":[158],"Thus,":[159],"it":[160],"aimed":[162],"make":[164],"fast":[165],"efficient":[167],"line.":[173]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
