{"id":"https://openalex.org/W4385484746","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191941","title":"Detection of Electric Component Based on Improved Faster-RCNN","display_name":"Detection of Electric Component Based on Improved Faster-RCNN","publication_year":2023,"publication_date":"2023-06-18","ids":{"openalex":"https://openalex.org/W4385484746","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191941"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn54540.2023.10191941","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn54540.2023.10191941","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5091082376","display_name":"Chengling Xiao","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengling Xiao","raw_affiliation_strings":["Tongji University, College of Computer Science and Technology,Shanghai,China","Tongji University, College of Computer Science and Technology, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tongji University, College of Computer Science and Technology,Shanghai,China","institution_ids":["https://openalex.org/I116953780"]},{"raw_affiliation_string":"Tongji University, College of Computer Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100416380","display_name":"Dongdong Zhang","orcid":"https://orcid.org/0000-0002-8619-2140"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongdong Zhang","raw_affiliation_strings":["Tongji University, College of Computer Science and Technology,Shanghai,China","Tongji University, College of Computer Science and Technology, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tongji University, College of Computer Science and Technology,Shanghai,China","institution_ids":["https://openalex.org/I116953780"]},{"raw_affiliation_string":"Tongji University, College of Computer Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023062351","display_name":"Chengyu Sun","orcid":"https://orcid.org/0000-0002-5686-5957"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengyu Sun","raw_affiliation_strings":["Tongji University,Shanghai Key Laboratory of Urban Renewal and Spatial Optimization Technology,Shanghai,China","Shanghai Key Laboratory of Urban Renewal and Spatial Optimization Technology, Tongji University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tongji University,Shanghai Key Laboratory of Urban Renewal and Spatial Optimization Technology,Shanghai,China","institution_ids":["https://openalex.org/I116953780"]},{"raw_affiliation_string":"Shanghai Key Laboratory of Urban Renewal and Spatial Optimization Technology, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I116953780"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"28","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9991999864578247,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9894999861717224,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9872999787330627,"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/computer-science","display_name":"Computer science","score":0.7224719524383545},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7179040908813477},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.5702254772186279},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5637180805206299},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.5261663198471069},{"id":"https://openalex.org/keywords/block-diagram","display_name":"Block diagram","score":0.4669066071510315},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4525257647037506},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.4365997612476349},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.406579852104187},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4059796929359436},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4001883864402771},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32477790117263794},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1439114511013031}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7224719524383545},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7179040908813477},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.5702254772186279},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5637180805206299},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.5261663198471069},{"id":"https://openalex.org/C149227320","wikidata":"https://www.wikidata.org/wiki/Q884718","display_name":"Block diagram","level":2,"score":0.4669066071510315},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4525257647037506},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.4365997612476349},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.406579852104187},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4059796929359436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4001883864402771},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32477790117263794},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1439114511013031},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn54540.2023.10191941","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn54540.2023.10191941","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1964428975","https://openalex.org/W1977512039","https://openalex.org/W2052958516","https://openalex.org/W2073459066","https://openalex.org/W2102605133","https://openalex.org/W2109255472","https://openalex.org/W2143238378","https://openalex.org/W2368821632","https://openalex.org/W2391168189","https://openalex.org/W2560311620","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2618530766","https://openalex.org/W2665599524","https://openalex.org/W2939388529","https://openalex.org/W2963037989","https://openalex.org/W2963315052","https://openalex.org/W3106250896","https://openalex.org/W6620707391","https://openalex.org/W6637373629","https://openalex.org/W6708015761","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2000785801","https://openalex.org/W986318368","https://openalex.org/W2384410913","https://openalex.org/W2352878646","https://openalex.org/W2004734601","https://openalex.org/W2130149817","https://openalex.org/W2990194547","https://openalex.org/W1480123525","https://openalex.org/W2620865396","https://openalex.org/W2414054180"],"abstract_inverted_index":{"In":[0,111,162],"the":[1,14,19,25,33,40,50,71,116,128,140,155,163,166,178,187,192,195,203],"era":[2],"of":[3,18,24,39,74,89,123,158,180],"smart":[4],"grid":[5,20,189],"development,":[6],"it":[7],"is":[8,32,58,63,79,169,199],"an":[9,101],"important":[10],"task":[11],"to":[12,29,47,49,65,105,113,153,171,191,201],"improve":[13,115],"efficiency":[15,118],"and":[16,35,62,82,92,143],"intelligence":[17],"control":[21],"system.":[22],"One":[23],"problems":[26],"that":[27],"need":[28],"be":[30],"solved":[31],"tedious":[34],"error-prone":[36],"manual":[37,55],"drawing":[38,190],"CAD":[41],"design":[42],"diagram,":[43],"which":[44],"requires":[45],"maintainers":[46],"refer":[48],"original":[51,129,188],"electrical":[52,75,107,124,160,205],"diagram":[53],"for":[54,119],"drawing.":[56],"This":[57],"a":[59,120,145],"huge":[60],"workload":[61],"prone":[64],"mistakes.":[66],"To":[67,94,176],"solve":[68],"this":[69],"problem,":[70],"automatic":[72,97],"identification":[73],"component":[76],"in":[77,87,109],"diagrams":[78],"particularly":[80],"critical,":[81],"existing":[83],"methods":[84],"are":[85],"inadequate":[86],"terms":[88],"algorithmic":[90],"accuracy":[91],"robustness.":[93],"achieve":[95],"better":[96,172],"detection,":[98],"we":[99,126],"propose":[100],"improved":[102],"Faster-RCNN":[103],"algorithm":[104,168],"detect":[106,202],"components":[108],"diagrams.":[110],"order":[112],"further":[114],"recognition":[117],"multiple":[121],"scales":[122],"components,":[125],"replaced":[127],"feature":[130,141,146],"extractor":[131,142],"VGG-16(Visual":[132],"Geometry":[133],"Group":[134],"16-layer":[135],"model)":[136],"with":[137],"ResNet-50":[138],"as":[139],"introduce":[144],"fusion":[147],"network":[148],"based":[149],"on":[150],"attention":[151],"block":[152],"strengthen":[154],"detection":[156],"ability":[157],"multi-size":[159],"components.":[161],"RPN":[164],"network,":[165],"k-means++":[167],"introduced":[170],"generate":[173],"anchor":[174],"boxes.":[175],"overcome":[177],"problem":[179],"information":[181],"loss":[182],"caused":[183],"by":[184],"directly":[185],"reducing":[186],"predetermined":[193],"size,":[194],"overlapping":[196],"sliding":[197],"window":[198],"used":[200],"high-resolution":[204],"diagram.":[206]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
