{"id":"https://openalex.org/W2790899428","doi":"https://doi.org/10.3233/jifs-17592","title":"Effective vehicle logo recognition in real-world application using mapreduce based convolutional neural networks with a pre-training strategy","display_name":"Effective vehicle logo recognition in real-world application using mapreduce based convolutional neural networks with a pre-training strategy","publication_year":2018,"publication_date":"2018-03-22","ids":{"openalex":"https://openalex.org/W2790899428","doi":"https://doi.org/10.3233/jifs-17592","mag":"2790899428"},"language":"en","primary_location":{"id":"doi:10.3233/jifs-17592","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-17592","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent &amp; Fuzzy Systems","raw_type":"journal-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/A5061108304","display_name":"Binquan Li","orcid":"https://orcid.org/0000-0002-9958-0396"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Binquan Li","raw_affiliation_strings":["School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, HaiDian District, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, HaiDian District, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100740224","display_name":"Xiaohui Hu","orcid":"https://orcid.org/0000-0001-5717-8676"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210128818","display_name":"Institute of Software","ror":"https://ror.org/033dfsn42","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128818"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaohui Hu","raw_affiliation_strings":["The Institute of Software, Chinese Academy of Sciences, Zhong Guan Cun, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Institute of Software, Chinese Academy of Sciences, Zhong Guan Cun, Beijing, China","institution_ids":["https://openalex.org/I4210128818","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5061108304"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.8173,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.77037124,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"34","issue":"3","first_page":"1985","last_page":"1994"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12707","display_name":"Vehicle License Plate Recognition","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T12707","display_name":"Vehicle License Plate Recognition","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9990000128746033,"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":0.8841472864151001},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6135434508323669},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.589484691619873},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5621771812438965},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5150732398033142},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.46329817175865173},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.41801977157592773},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.4128304123878479},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3948816657066345},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.345397412776947}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8841472864151001},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6135434508323669},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.589484691619873},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5621771812438965},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5150732398033142},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.46329817175865173},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.41801977157592773},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.4128304123878479},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3948816657066345},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.345397412776947},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jifs-17592","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-17592","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent &amp; Fuzzy Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W855165587","https://openalex.org/W1498436455","https://openalex.org/W1932847118","https://openalex.org/W1976948919","https://openalex.org/W1984020445","https://openalex.org/W2022508996","https://openalex.org/W2068470708","https://openalex.org/W2069428064","https://openalex.org/W2080829704","https://openalex.org/W2088920913","https://openalex.org/W2097117768","https://openalex.org/W2107093743","https://openalex.org/W2110177572","https://openalex.org/W2111051539","https://openalex.org/W2116456623","https://openalex.org/W2120432001","https://openalex.org/W2122451825","https://openalex.org/W2124450022","https://openalex.org/W2125085157","https://openalex.org/W2126715624","https://openalex.org/W2131241448","https://openalex.org/W2132424367","https://openalex.org/W2138857742","https://openalex.org/W2139323304","https://openalex.org/W2141125852","https://openalex.org/W2145287260","https://openalex.org/W2147768505","https://openalex.org/W2154753510","https://openalex.org/W2163605009","https://openalex.org/W2168117308","https://openalex.org/W2170135819","https://openalex.org/W2221532093","https://openalex.org/W2543461915","https://openalex.org/W2962911728","https://openalex.org/W2963542991","https://openalex.org/W4231109964"],"related_works":["https://openalex.org/W2989932438","https://openalex.org/W3081496756","https://openalex.org/W3099765033","https://openalex.org/W3199608561","https://openalex.org/W4210794429","https://openalex.org/W3012393889","https://openalex.org/W3127819136","https://openalex.org/W2767651786","https://openalex.org/W4225852842","https://openalex.org/W2742991909"],"abstract_inverted_index":{"Large":[0],"amounts":[1],"of":[2,20,29,94,167,216],"data":[3],"are":[4,211],"generated":[5],"by":[6,90,198],"the":[7,15,27,41,58,82,91,122,127,132,165,172,176,183,190,204],"intelligent":[8],"transportation":[9],"system":[10,104,206],"(ITS)":[11],"everyday.":[12],"It":[13],"exceeds":[14],"storage":[16],"and":[17,23,71,98,110,130,155,174,188,208],"processing":[18],"capacity":[19],"conventional":[21],"systems,":[22],"also":[24],"doesn\u2019t":[25],"fit":[26],"structures":[28],"current":[30],"database.":[31],"Therefore,":[32],"it":[33],"is":[34,47,54],"necessary":[35],"to":[36,57,120,163],"use":[37],"efficient":[38],"methodology":[39],"addressing":[40],"challenges.":[42],"Vehicle":[43],"logo":[44],"recognition":[45,191],"(VLR)":[46],"a":[48,100,114,143,148],"significant":[49],"application":[50],"in":[51,84,161],"ITS.":[52],"VLR":[53,102],"difficult":[55],"due":[56],"geometric":[59],"distortions":[60],"as":[61,63],"well":[62],"various":[64],"imaging":[65],"situations":[66],"simultaneously.":[67,135],"However,":[68],"traditional":[69],"methods":[70],"hand-crafted":[72],"features":[73],"have":[74],"many":[75,85],"limitations.":[76],"Convolutional":[77],"neural":[78],"network":[79],"(CNN)":[80],"enjoys":[81],"success":[83],"machine":[86],"vision":[87],"tasks.":[88],"Inspired":[89],"excellent":[92],"performance":[93],"CNN,":[95],"we":[96,146],"design":[97],"develop":[99],"novel":[101,149],"distributed":[103,205],"framework":[105,207],"based":[106,116],"on":[107],"Hadoop":[108],"ecosystem":[109],"deeplearning.":[111],"We":[112],"propose":[113,147],"Mapreduce":[115],"CNN":[117,140],"called":[118,159],"MRCNN":[119],"train":[121],"networks,":[123],"which":[124,169],"significantly":[125],"increases":[126,189],"training":[128],"speed":[129],"reduces":[131],"computation":[133],"cost":[134],"Furthermore,":[136],"unlike":[137],"previous":[138],"classical":[139],"starting":[141],"from":[142],"random":[144],"initialization,":[145],"genetic":[150],"algorithm":[151],"(GA)":[152],"global":[153],"optimization":[154],"Bayesian":[156],"regularization":[157],"approach":[158],"GABR":[160],"order":[162],"initialize":[164],"weights":[166,196],"classifier,":[168],"help":[170],"prevent":[171],"overfitting":[173],"avoid":[175],"local":[177],"optima.":[178],"Compared":[179],"with":[180,193],"other":[181],"algorithms,":[182],"proposed":[184,209],"method":[185],"performs":[186],"best":[187],"accuracy":[192],"good":[194],"initial":[195],"optimized":[197],"GABR.":[199],"The":[200],"results":[201],"show":[202],"that":[203],"algorithms":[210],"suitable":[212],"for":[213],"real-world":[214],"applications":[215],"VLR.":[217]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
