{"id":"https://openalex.org/W4220773165","doi":"https://doi.org/10.1007/s11042-022-12163-0","title":"Traffic sign recognition based on deep learning","display_name":"Traffic sign recognition based on deep learning","publication_year":2022,"publication_date":"2022-03-07","ids":{"openalex":"https://openalex.org/W4220773165","doi":"https://doi.org/10.1007/s11042-022-12163-0"},"language":"en","primary_location":{"id":"doi:10.1007/s11042-022-12163-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11042-022-12163-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11042-022-12163-0.pdf","source":{"id":"https://openalex.org/S110206669","display_name":"Multimedia Tools and Applications","issn_l":"1380-7501","issn":["1380-7501","1573-7721"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multimedia Tools and Applications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11042-022-12163-0.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110700731","display_name":"Yanzhao Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I39854758","display_name":"Auckland University of Technology","ror":"https://ror.org/01zvqw119","country_code":"NZ","type":"education","lineage":["https://openalex.org/I39854758"]}],"countries":["NZ"],"is_corresponding":true,"raw_author_name":"Yanzhao Zhu","raw_affiliation_strings":["Auckland University of Technology, CBD, Auckland, New Zealand"],"affiliations":[{"raw_affiliation_string":"Auckland University of Technology, CBD, Auckland, New Zealand","institution_ids":["https://openalex.org/I39854758"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064286235","display_name":"Wei Qi Yan","orcid":"https://orcid.org/0009-0006-5891-9919"},"institutions":[{"id":"https://openalex.org/I39854758","display_name":"Auckland University of Technology","ror":"https://ror.org/01zvqw119","country_code":"NZ","type":"education","lineage":["https://openalex.org/I39854758"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Wei Qi Yan","raw_affiliation_strings":["Auckland University of Technology, CBD, Auckland, New Zealand"],"affiliations":[{"raw_affiliation_string":"Auckland University of Technology, CBD, Auckland, New Zealand","institution_ids":["https://openalex.org/I39854758"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5110700731"],"corresponding_institution_ids":["https://openalex.org/I39854758"],"apc_list":null,"apc_paid":null,"fwci":19.1002,"has_fulltext":true,"cited_by_count":195,"citation_normalized_percentile":{"value":0.99676517,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"81","issue":"13","first_page":"17779","last_page":"17791"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9991999864578247,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9980000257492065,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.917959451675415},{"id":"https://openalex.org/keywords/traffic-sign-recognition","display_name":"Traffic sign recognition","score":0.8769222497940063},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6957895755767822},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6865418553352356},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5674898028373718},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.5621410608291626},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4904906451702118},{"id":"https://openalex.org/keywords/sign","display_name":"Sign (mathematics)","score":0.4873412549495697},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.46888136863708496},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.4478142261505127},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.44678014516830444},{"id":"https://openalex.org/keywords/traffic-sign","display_name":"Traffic sign","score":0.4342733919620514},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42543739080429077},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4237549304962158},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4202851951122284}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.917959451675415},{"id":"https://openalex.org/C6528762","wikidata":"https://www.wikidata.org/wiki/Q1574298","display_name":"Traffic sign recognition","level":4,"score":0.8769222497940063},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6957895755767822},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6865418553352356},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5674898028373718},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.5621410608291626},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4904906451702118},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.4873412549495697},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.46888136863708496},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.4478142261505127},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.44678014516830444},{"id":"https://openalex.org/C2983860417","wikidata":"https://www.wikidata.org/wiki/Q170285","display_name":"Traffic sign","level":3,"score":0.4342733919620514},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42543739080429077},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4237549304962158},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4202851951122284},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s11042-022-12163-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11042-022-12163-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11042-022-12163-0.pdf","source":{"id":"https://openalex.org/S110206669","display_name":"Multimedia Tools and Applications","issn_l":"1380-7501","issn":["1380-7501","1573-7721"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multimedia Tools and Applications","raw_type":"journal-article"},{"id":"pmh:oai:openrepository.aut.ac.nz:10292/15247","is_oa":true,"landing_page_url":"https://hdl.handle.net/10292/15247","pdf_url":null,"source":{"id":"https://openalex.org/S4306401809","display_name":"Tuwhera (Auckland University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39854758","host_organization_name":"Auckland University of Technology","host_organization_lineage":["https://openalex.org/I39854758"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Journal Article"}],"best_oa_location":{"id":"doi:10.1007/s11042-022-12163-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11042-022-12163-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11042-022-12163-0.pdf","source":{"id":"https://openalex.org/S110206669","display_name":"Multimedia Tools and Applications","issn_l":"1380-7501","issn":["1380-7501","1573-7721"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multimedia Tools and Applications","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.5600000023841858,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320310339","display_name":"Auckland University of Technology, New Zealand","ror":"https://ror.org/01zvqw119"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4220773165.pdf","grobid_xml":"https://content.openalex.org/works/W4220773165.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W2100495367","https://openalex.org/W2193145675","https://openalex.org/W2593341394","https://openalex.org/W2817549221","https://openalex.org/W2897142180","https://openalex.org/W2898296256","https://openalex.org/W2903300682","https://openalex.org/W2907483817","https://openalex.org/W2910199258","https://openalex.org/W2942180685","https://openalex.org/W2963037989","https://openalex.org/W2963898379","https://openalex.org/W2964601876","https://openalex.org/W2979395867","https://openalex.org/W2996852484","https://openalex.org/W3009899240","https://openalex.org/W3043769718","https://openalex.org/W3105650422","https://openalex.org/W3106250896","https://openalex.org/W3109992649","https://openalex.org/W3117123720","https://openalex.org/W3126502934","https://openalex.org/W3136643320","https://openalex.org/W3139267523","https://openalex.org/W3145297811","https://openalex.org/W3157144426","https://openalex.org/W3158114921","https://openalex.org/W3159144758","https://openalex.org/W3163634295","https://openalex.org/W3166744172"],"related_works":["https://openalex.org/W4382897155","https://openalex.org/W4283820116","https://openalex.org/W4379231512","https://openalex.org/W4378699879","https://openalex.org/W3128164723","https://openalex.org/W4286647459","https://openalex.org/W2899819381","https://openalex.org/W2557202782","https://openalex.org/W3215426395","https://openalex.org/W4382176313"],"abstract_inverted_index":{"Abstract":[0],"Intelligent":[1],"Transportation":[2],"System":[3],"(ITS),":[4],"including":[5],"unmanned":[6],"vehicles,":[7],"has":[8,56,73],"been":[9],"gradually":[10],"matured":[11],"despite":[12],"on":[13,47,69,100],"road.":[14],"How":[15],"to":[16,21,89,151],"eliminate":[17],"the":[18,76,91,94,111,136,152,170],"interference":[19],"due":[20],"various":[22],"environmental":[23],"factors,":[24],"carry":[25],"out":[26],"accurate":[27],"and":[28,33,53],"efficient":[29],"traffic":[30],"sign":[31],"detection":[32],"recognition,":[34],"is":[35,120],"a":[36,125],"key":[37],"technical":[38],"problem.":[39],"However,":[40],"traditional":[41],"visual":[42,48,65,114],"object":[43,66,80,115],"recognition":[44,67,116,175],"mainly":[45],"relies":[46],"feature":[49],"extraction,":[50],"e.g.,":[51],"color":[52],"edge,":[54],"which":[55,72,108],"limitations.":[57],"Convolutional":[58],"neural":[59],"network":[60],"(CNN)":[61],"was":[62],"designed":[63],"for":[64,103,113,122,162],"based":[68,99],"deep":[70,118],"learning,":[71],"successfully":[74],"overcome":[75],"shortcomings":[77],"of":[78,93,97,138,160],"conventional":[79],"recognition.":[81],"In":[82],"this":[83,139,144],"paper,":[84],"we":[85],"implement":[86],"an":[87],"experiment":[88],"evaluate":[90],"performance":[92],"latest":[95],"version":[96],"YOLOv5":[98,155,177],"our":[101,147],"dataset":[102],"Traffic":[104],"Sign":[105],"Recognition":[106],"(TSR),":[107],"unfolds":[109],"how":[110],"model":[112],"in":[117,143,158,169],"learning":[119],"suitable":[121],"TSR":[123],"through":[124],"comprehensive":[126],"comparison":[127],"with":[128],"SSD":[129,165],"(i.e.,":[130],"single":[131],"shot":[132],"multibox":[133],"detector)":[134],"as":[135],"objective":[137],"paper.":[140],"The":[141],"experiments":[142],"project":[145],"utilize":[146],"own":[148],"dataset.":[149],"Pertaining":[150],"experimental":[153],"results,":[154],"achieves":[156],"97.70%":[157],"terms":[159],"mAP@0.5":[161],"all":[163],"classes,":[164],"obtains":[166],"90.14%":[167],"mAP":[168],"same":[171],"term.":[172],"Meanwhile,":[173],"regarding":[174],"speed,":[176],"also":[178],"outperforms":[179],"SSD.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":59},{"year":2024,"cited_by_count":61},{"year":2023,"cited_by_count":56},{"year":2022,"cited_by_count":11},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}
