{"id":"https://openalex.org/W2949472531","doi":"https://doi.org/10.23919/eusipco.2019.8903139","title":"Power Distribution Insulators Classification Using Image Hybrid Deep Learning","display_name":"Power Distribution Insulators Classification Using Image Hybrid Deep Learning","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2949472531","doi":"https://doi.org/10.23919/eusipco.2019.8903139","mag":"2949472531"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco.2019.8903139","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2019.8903139","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 27th European Signal Processing Conference (EUSIPCO)","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/A5030940663","display_name":"E. Furtado De Simas Filho","orcid":"https://orcid.org/0000-0001-8707-785X"},"institutions":[{"id":"https://openalex.org/I126158947","display_name":"Universidade Federal da Bahia","ror":"https://ror.org/03k3p7647","country_code":"BR","type":"education","lineage":["https://openalex.org/I126158947"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Eduardo F. Simas Filho","raw_affiliation_strings":["Digital Systems Laboratory, PPGEE, Federal University of Bahia, Salvador, Brazil"],"affiliations":[{"raw_affiliation_string":"Digital Systems Laboratory, PPGEE, Federal University of Bahia, Salvador, Brazil","institution_ids":["https://openalex.org/I126158947"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040172163","display_name":"Ricardo M. Prates","orcid":"https://orcid.org/0000-0002-1580-9828"},"institutions":[{"id":"https://openalex.org/I126158947","display_name":"Universidade Federal da Bahia","ror":"https://ror.org/03k3p7647","country_code":"BR","type":"education","lineage":["https://openalex.org/I126158947"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Ricardo M. Prates","raw_affiliation_strings":["Digital Systems Laboratory, PPGEE, Federal University of Bahia, Salvador, Brazil"],"affiliations":[{"raw_affiliation_string":"Digital Systems Laboratory, PPGEE, Federal University of Bahia, Salvador, Brazil","institution_ids":["https://openalex.org/I126158947"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066366546","display_name":"Rodrigo Pereira Ramos","orcid":"https://orcid.org/0000-0002-9243-3887"},"institutions":[{"id":"https://openalex.org/I59530521","display_name":"Universidade Federal do Vale do S\u00e3o Francisco","ror":"https://ror.org/00devjr72","country_code":"BR","type":"education","lineage":["https://openalex.org/I59530521"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rodrigo P. Ramos","raw_affiliation_strings":["Federal University of Vale do S\u00e3o Francisco, Petrolina, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Vale do S\u00e3o Francisco, Petrolina, Brazil","institution_ids":["https://openalex.org/I59530521"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013827007","display_name":"Jaime S. Cardoso","orcid":"https://orcid.org/0000-0002-3760-2473"},"institutions":[{"id":"https://openalex.org/I182534213","display_name":"Universidade do Porto","ror":"https://ror.org/043pwc612","country_code":"PT","type":"education","lineage":["https://openalex.org/I182534213"]},{"id":"https://openalex.org/I4210166615","display_name":"INESC TEC","ror":"https://ror.org/05fa8ka61","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210125590","https://openalex.org/I4210166615"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Jaime S. Cardoso","raw_affiliation_strings":["INESC TEC, Portugal Faculty of Engineering, University of Porto, Portugal"],"affiliations":[{"raw_affiliation_string":"INESC TEC, Portugal Faculty of Engineering, University of Porto, Portugal","institution_ids":["https://openalex.org/I4210166615","https://openalex.org/I182534213"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5030940663"],"corresponding_institution_ids":["https://openalex.org/I126158947"],"apc_list":null,"apc_paid":null,"fwci":0.2011,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.56105958,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9988999962806702,"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":0.9988999962806702,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.996399998664856,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/computer-science","display_name":"Computer science","score":0.7810341119766235},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6952232718467712},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6950880289077759},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6669993996620178},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6180533170700073},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5589461326599121},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.5505735874176025},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5123180150985718},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.48215052485466003},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4740293323993683},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4517771303653717},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4517599046230316},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4457366466522217},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.16397958993911743},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12922877073287964}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7810341119766235},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6952232718467712},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6950880289077759},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6669993996620178},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6180533170700073},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5589461326599121},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.5505735874176025},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5123180150985718},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.48215052485466003},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4740293323993683},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4517771303653717},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4517599046230316},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4457366466522217},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16397958993911743},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12922877073287964},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco.2019.8903139","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2019.8903139","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 27th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1486242743","https://openalex.org/W1522301498","https://openalex.org/W2014695103","https://openalex.org/W2065039610","https://openalex.org/W2090560159","https://openalex.org/W2104315030","https://openalex.org/W2112324576","https://openalex.org/W2129798396","https://openalex.org/W2137664016","https://openalex.org/W2149298154","https://openalex.org/W2150418681","https://openalex.org/W2158698691","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2253429366","https://openalex.org/W2558580397","https://openalex.org/W2580931256","https://openalex.org/W2711924351","https://openalex.org/W2790977031","https://openalex.org/W2896011954","https://openalex.org/W2897427890","https://openalex.org/W2911964244","https://openalex.org/W2964121744","https://openalex.org/W4251133453","https://openalex.org/W4298254629","https://openalex.org/W6631190155"],"related_works":["https://openalex.org/W2945706271","https://openalex.org/W4295705264","https://openalex.org/W4387435415","https://openalex.org/W2114169842","https://openalex.org/W2535808783","https://openalex.org/W3176438653","https://openalex.org/W2981628807","https://openalex.org/W3012393889","https://openalex.org/W4379875147","https://openalex.org/W2005234362"],"abstract_inverted_index":{"The":[0,154],"Overhead":[1],"Power":[2],"Distribution":[3],"Lines":[4],"present":[5],"a":[6,59,112,118,126,131,158],"wide":[7],"range":[8],"of":[9,18,47,75,89,101,134,171],"insulator":[10,71],"components,":[11],"which":[12,65,143],"have":[13],"different":[14],"shapes":[15],"and":[16,28,99],"types":[17],"building":[19],"materials.":[20],"These":[21,41],"components":[22],"are":[23],"usually":[24],"exposed":[25],"to":[26,105,141],"weather":[27],"operational":[29],"conditions":[30],"that":[31],"may":[32],"cause":[33],"deviations":[34],"in":[35,161,168],"their":[36],"shapes,":[37],"colors":[38],"or":[39],"textures.":[40],"changes":[42],"might":[43],"hinder":[44],"the":[45,68,85,107,147,169],"development":[46],"automatic":[48],"systems":[49],"for":[50,62,122,129,151],"visual":[51],"inspection.":[52],"In":[53,110],"this":[54,56],"perspective,":[55],"work":[57,80],"presents":[58],"robust":[60],"methodology":[61,156],"image":[63],"classification,":[64],"aims":[66],"at":[67],"efficient":[69],"distribution":[70],"class":[72],"identification,":[73],"regardless":[74],"its":[76],"degradation":[77],"level.":[78],"This":[79],"can":[81],"be":[82],"characterized":[83],"by":[84],"following":[86],"steps:":[87],"implementation":[88],"Convolutional":[90],"Neural":[91],"Network":[92],"(CNN);":[93],"transfer":[94],"learning;":[95],"attribute":[96],"vector":[97],"acquisition":[98],"design":[100],"hybrid":[102],"classifier":[103,144],"architectures":[104],"improve":[106],"discrimination":[108,149],"efficiency.":[109],"summary,":[111],"previously":[113],"trained":[114],"CNN":[115],"goes":[116],"through":[117],"fine":[119],"tuning":[120],"stage":[121],"later":[123],"use":[124],"as":[125],"feature":[127],"extractor":[128],"training":[130],"new":[132],"set":[133],"classifiers.":[135],"A":[136],"comparative":[137],"study":[138],"was":[139],"conducted":[140],"identify":[142],"architecture":[145],"obtained":[146],"best":[148],"performance":[150],"non-conforming":[152,172],"components.":[153],"proposed":[155],"showed":[157],"significant":[159],"improvement":[160],"classification":[162],"performance,":[163],"obtaining":[164],"95%":[165],"overall":[166],"accuracy":[167],"identification":[170],"component":[173],"classes.":[174]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}