{"id":"https://openalex.org/W4362693127","doi":"https://doi.org/10.1109/tnnls.2023.3261363","title":"Fusion PCAM R-CNN of Automatic Segmentation for Magnetic Flux Leakage Defects","display_name":"Fusion PCAM R-CNN of Automatic Segmentation for Magnetic Flux Leakage Defects","publication_year":2023,"publication_date":"2023-04-07","ids":{"openalex":"https://openalex.org/W4362693127","doi":"https://doi.org/10.1109/tnnls.2023.3261363","pmid":"https://pubmed.ncbi.nlm.nih.gov/37027265"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2023.3261363","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2023.3261363","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5100717501","display_name":"Zhujun Wang","orcid":"https://orcid.org/0000-0001-8008-5654"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhujun Wang","raw_affiliation_strings":["School of Automation, Shenyang Aerospace University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0001-8008-5654","affiliations":[{"raw_affiliation_string":"School of Automation, Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Lijian Yang","orcid":"https://orcid.org/0000-0002-1616-3299"},"institutions":[{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lijian Yang","raw_affiliation_strings":["School of Information Science and Technology, Shenyang University of Technology, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-1616-3299","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Shenyang University of Technology, Shenyang, China","institution_ids":["https://openalex.org/I157507598"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100639225","display_name":"Tianhe Sun","orcid":"https://orcid.org/0000-0002-8077-1301"},"institutions":[{"id":"https://openalex.org/I108904480","display_name":"Shenyang Institute of Engineering","ror":"https://ror.org/02pfsj857","country_code":"CN","type":"education","lineage":["https://openalex.org/I108904480"]},{"id":"https://openalex.org/I4210128819","display_name":"Institute of Electrical Engineering","ror":"https://ror.org/033js6g46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128819"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianhe Sun","raw_affiliation_strings":["Institute of Electrical Engineering, Shenyang Institute of Engineering, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Electrical Engineering, Shenyang Institute of Engineering, Shenyang, China","institution_ids":["https://openalex.org/I108904480","https://openalex.org/I4210128819"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090124818","display_name":"Weizhe Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weizhe Yan","raw_affiliation_strings":["Department of Construction, State Grid Tianjin Dongli Company, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Construction, State Grid Tianjin Dongli Company, Tianjin, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3265,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.76936545,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"35","issue":"8","first_page":"11424","last_page":"11435"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9991999864578247,"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"}},{"id":"https://openalex.org/T12086","display_name":"Structural Integrity and Reliability Analysis","score":0.9775000214576721,"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"}},{"id":"https://openalex.org/T10834","display_name":"Welding Techniques and Residual Stresses","score":0.9750999808311462,"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/magnetic-flux-leakage","display_name":"Magnetic flux leakage","score":0.7888355255126953},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6936594247817993},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6816760897636414},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6145462393760681},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5684948563575745},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5684031844139099},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5633129477500916},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5155763030052185},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4380725622177124},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.43330785632133484},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.270499050617218},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1904022991657257},{"id":"https://openalex.org/keywords/magnetic-field","display_name":"Magnetic field","score":0.09076961874961853},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07888445258140564}],"concepts":[{"id":"https://openalex.org/C20892748","wikidata":"https://www.wikidata.org/wiki/Q4390394","display_name":"Magnetic flux leakage","level":3,"score":0.7888355255126953},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6936594247817993},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6816760897636414},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6145462393760681},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5684948563575745},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5684031844139099},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5633129477500916},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5155763030052185},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4380725622177124},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.43330785632133484},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.270499050617218},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1904022991657257},{"id":"https://openalex.org/C115260700","wikidata":"https://www.wikidata.org/wiki/Q11408","display_name":"Magnetic field","level":2,"score":0.09076961874961853},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07888445258140564},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2023.3261363","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2023.3261363","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:37027265","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37027265","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8171295526","display_name":null,"funder_award_id":"62003224","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2041053548","https://openalex.org/W2092898710","https://openalex.org/W2106646388","https://openalex.org/W2126827132","https://openalex.org/W2136922672","https://openalex.org/W2221448138","https://openalex.org/W2320725294","https://openalex.org/W2412782625","https://openalex.org/W2518378845","https://openalex.org/W2553446265","https://openalex.org/W2554156454","https://openalex.org/W2595750449","https://openalex.org/W2604486661","https://openalex.org/W2605231856","https://openalex.org/W2798117183","https://openalex.org/W2805838226","https://openalex.org/W2901931791","https://openalex.org/W2906103050","https://openalex.org/W2963631169","https://openalex.org/W2963881378","https://openalex.org/W2972139070","https://openalex.org/W3000983008","https://openalex.org/W3024127960","https://openalex.org/W3032966648","https://openalex.org/W3046421354","https://openalex.org/W3049282540","https://openalex.org/W3087884309","https://openalex.org/W3092447054","https://openalex.org/W3094071795","https://openalex.org/W3112721793","https://openalex.org/W3128131898","https://openalex.org/W3138986594","https://openalex.org/W3159998681","https://openalex.org/W4293107381","https://openalex.org/W4296211514","https://openalex.org/W4309413706","https://openalex.org/W6715287400"],"related_works":["https://openalex.org/W4383333701","https://openalex.org/W803998858","https://openalex.org/W3005508687","https://openalex.org/W2142368441","https://openalex.org/W4386858688","https://openalex.org/W2982536526","https://openalex.org/W4380302312","https://openalex.org/W3008689640","https://openalex.org/W4385338604","https://openalex.org/W3081626085"],"abstract_inverted_index":{"Magnetic":[0],"leakage":[1,25],"detection":[2,21,47],"technology":[3],"plays":[4],"an":[5,55],"important":[6],"role":[7],"in":[8,60,114,159,169],"the":[9,20,44,78,87,95,111,115,123,133,137,140,145,151,160,170],"long-oil":[10],"pipeline.":[11],"Automatic":[12],"segmentation":[13,31,92],"of":[14,22,94,102,139,154,172],"defecting":[15],"images":[16],"is":[17,58,83,105,157],"crucial":[18],"for":[19,32],"magnetic":[23],"flux":[24],"(MFL)":[26],"works.":[27],"At":[28],"present,":[29],"accurate":[30],"small":[33],"defects":[34,156],"has":[35],"always":[36],"been":[37],"a":[38],"difficult":[39],"problem.":[40],"In":[41],"contrast":[42],"to":[43,85,107,143],"state-of-the-art":[45],"MFL":[46,155,173],"methodologies":[48],"based":[49],"on":[50],"convolution":[51,96,112,161],"neural":[52],"network":[53,91,135],"(CNN),":[54],"optimization":[56],"method":[57],"devised":[59],"our":[61],"study":[62],"by":[63],"integrating":[64],"mask":[65],"region-based":[66],"CNN":[67],"(Mask":[68],"R-CNN)":[69],"and":[70,90],"information":[71,103],"entropy":[72,104],"constraint":[73,100],"(IEC).":[74],"To":[75],"be":[76,108,167],"precise,":[77],"principal":[79],"component":[80],"analysis":[81],"(PCA)":[82],"utilized":[84],"improve":[86],"feature":[88,141,147,152],"learning":[89],"ability":[93],"kernel.":[97],"The":[98,119,163],"similarity":[99],"rule":[101],"proposed":[106],"inserted":[109],"into":[110],"layer":[113],"Mask":[116,120],"R-CNN":[117,121],"network.":[118],"optimizes":[122],"convolutional":[124],"kernel":[125],"with":[126],"similar":[127],"weights":[128],"or":[129],"higher":[130],"similarity,":[131],"meanwhile,":[132],"PCA":[134],"reduces":[136],"dimension":[138],"image":[142],"reconstruct":[144],"original":[146],"vector.":[148],"As":[149],"such,":[150],"extraction":[153],"optimized":[158],"check.":[162],"research":[164],"results":[165],"can":[166],"applied":[168],"field":[171],"detection.":[174]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}