{"id":"https://openalex.org/W3084946993","doi":"https://doi.org/10.1109/tits.2020.3020556","title":"Traffic Sign Recognition With Lightweight Two-Stage Model in Complex Scenes","display_name":"Traffic Sign Recognition With Lightweight Two-Stage Model in Complex Scenes","publication_year":2020,"publication_date":"2020-09-10","ids":{"openalex":"https://openalex.org/W3084946993","doi":"https://doi.org/10.1109/tits.2020.3020556","mag":"3084946993"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2020.3020556","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2020.3020556","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation 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/A5002397632","display_name":"Zhengshuai Wang","orcid":"https://orcid.org/0000-0001-5987-2039"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengshuai Wang","raw_affiliation_strings":["Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China","Institute for Artificial Intelligence, Tsinghua University, (THUAI), Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5987-2039","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Institute for Artificial Intelligence, Tsinghua University, (THUAI), Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100436368","display_name":"Jianqiang Wang","orcid":"https://orcid.org/0000-0003-4363-6108"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianqiang Wang","raw_affiliation_strings":["Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China","Institute for Artificial Intelligence, Tsinghua University, (THUAI), Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Institute for Artificial Intelligence, Tsinghua University, (THUAI), Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100346266","display_name":"Yali Li","orcid":"https://orcid.org/0000-0002-6629-7228"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yali Li","raw_affiliation_strings":["Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China","Institute for Artificial Intelligence, Tsinghua University, (THUAI), Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6629-7228","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Institute for Artificial Intelligence, Tsinghua University, (THUAI), Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030965866","display_name":"Shengjin Wang","orcid":"https://orcid.org/0000-0001-7809-1932"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengjin Wang","raw_affiliation_strings":["Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China","Institute for Artificial Intelligence, Tsinghua University, (THUAI), Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7809-1932","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Institute for Artificial Intelligence, Tsinghua University, (THUAI), Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":2.7243,"has_fulltext":false,"cited_by_count":52,"citation_normalized_percentile":{"value":0.89358809,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":100},"biblio":{"volume":"23","issue":"2","first_page":"1121","last_page":"1131"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9980999827384949,"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":0.9980999827384949,"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":0.9976000189781189,"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":0.9962000250816345,"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/traffic-sign-recognition","display_name":"Traffic sign recognition","score":0.7644215822219849},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7420352697372437},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6635903716087341},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.6438868641853333},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6094446778297424},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5779106616973877},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5231686234474182},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3263966143131256},{"id":"https://openalex.org/keywords/traffic-sign","display_name":"Traffic sign","score":0.2546825408935547},{"id":"https://openalex.org/keywords/sign","display_name":"Sign (mathematics)","score":0.18089666962623596},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10880771279335022}],"concepts":[{"id":"https://openalex.org/C6528762","wikidata":"https://www.wikidata.org/wiki/Q1574298","display_name":"Traffic sign recognition","level":4,"score":0.7644215822219849},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7420352697372437},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6635903716087341},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.6438868641853333},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6094446778297424},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5779106616973877},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5231686234474182},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3263966143131256},{"id":"https://openalex.org/C2983860417","wikidata":"https://www.wikidata.org/wiki/Q170285","display_name":"Traffic sign","level":3,"score":0.2546825408935547},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.18089666962623596},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10880771279335022},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2020.3020556","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2020.3020556","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6299999952316284}],"awards":[{"id":"https://openalex.org/G3195238014","display_name":null,"funder_award_id":"2019GQG0001","funder_id":"https://openalex.org/F4320322392","funder_display_name":"Tsinghua University"},{"id":"https://openalex.org/G8235566811","display_name":null,"funder_award_id":"61701277","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"},{"id":"https://openalex.org/F4320322392","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1655795019","https://openalex.org/W1861492603","https://openalex.org/W1966811077","https://openalex.org/W1988887431","https://openalex.org/W2012370837","https://openalex.org/W2027792236","https://openalex.org/W2032354522","https://openalex.org/W2044382768","https://openalex.org/W2067713319","https://openalex.org/W2099471712","https://openalex.org/W2102593897","https://openalex.org/W2102605133","https://openalex.org/W2114833239","https://openalex.org/W2117876524","https://openalex.org/W2125022514","https://openalex.org/W2125085157","https://openalex.org/W2144506857","https://openalex.org/W2150581781","https://openalex.org/W2152417180","https://openalex.org/W2157364932","https://openalex.org/W2174983451","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2222317246","https://openalex.org/W2396955114","https://openalex.org/W2479866714","https://openalex.org/W2538864697","https://openalex.org/W2625219738","https://openalex.org/W2752782242","https://openalex.org/W2769986739","https://openalex.org/W2962951509","https://openalex.org/W2975256828","https://openalex.org/W4293584584","https://openalex.org/W6629368666","https://openalex.org/W6714138976","https://openalex.org/W6750227808"],"related_works":["https://openalex.org/W2591697403","https://openalex.org/W2944728705","https://openalex.org/W2904022177","https://openalex.org/W2359348847","https://openalex.org/W3011538607","https://openalex.org/W4294432981","https://openalex.org/W4321441197","https://openalex.org/W4382897155","https://openalex.org/W4283820116","https://openalex.org/W2953716828"],"abstract_inverted_index":{"Traffic":[0],"sign":[1],"recognition":[2],"with":[3,51,212,223,253],"high":[4],"accuracy":[5],"and":[6,25,32,41,66,88,111,119,133,146,178,191],"real-time":[7],"is":[8,189,195,235,247],"an":[9],"important":[10],"part":[11],"of":[12,34,68,86,131,136,174,183,208,243],"the":[13,26,83,90,109,126,134,167,172,181,201,206,213,245],"intelligent":[14],"transportation":[15],"system.":[16],"In":[17],"this":[18],"article,":[19],"based":[20,161],"on":[21,162,200],"large-scale":[22],"traffic":[23,35],"signs":[24,69,87],"inherent":[27],"conflict":[28],"between":[29],"location":[30],"regression":[31],"classification":[33],"signs,":[36],"we":[37,140],"propose":[38,101,157],"a":[39,47,52,75,102,142,158,224],"novel":[40],"flexible":[42],"two-stage":[43,187],"approach.":[44,185,210],"It":[45,79,124,170],"combines":[46],"lightweight":[48,104,225],"superclass":[49,105],"detector":[50,226],"refinement":[53,159],"classifier.":[54],"The":[55,232],"main":[56],"contributions":[57],"lie":[58],"in":[59],"three":[60],"aspects:":[61],"(1)":[62],"We":[63,100,107,156],"use":[64],"locations":[65],"sizes":[67],"as":[70,93,95],"prior":[71],"knowledge":[72],"to":[73,150],"establish":[74],"probability":[76],"distribution":[77],"model.":[78],"can":[80],"significantly":[81],"decrease":[82],"search":[84],"range":[85],"improve":[89,152],"processing":[91,233],"speed,":[92],"well":[94],"reducing":[96],"false":[97],"detection.":[98],"(2)":[99],"high-performance":[103],"detector.":[106],"introduce":[108],"Inception":[110],"Channel":[112],"Attention,":[113],"by":[114,249],"generating":[115],"multi-scale":[116,147],"receptive":[117],"fields":[118],"adaptively":[120],"adjusting":[121],"channel":[122],"features.":[123],"alleviates":[125],"large":[127],"scale":[128],"variance":[129],"challenge":[130],"objects":[132],"interference":[135],"background":[137],"information.":[138],"Meanwhile,":[139],"present":[141],"merging":[143],"Batch":[144],"Normalization":[145],"testing":[148],"method":[149,217],"further":[151],"detection":[153],"performance.":[154],"(3)":[155],"classifier":[160],"similarity":[163],"measure":[164],"learning":[165],"for":[166],"subclass":[168],"classification.":[169],"increases":[171],"precision":[173],"discriminating":[175],"similar":[176],"subclasses":[177],"also":[179],"improves":[180],"extensibility":[182],"our":[184,209,216],"Our":[186],"approach":[188],"simple":[190],"effective,":[192],"whose":[193],"paradigm":[194],"different":[196],"from":[197],"others.":[198],"Experiments":[199],"Tsinghua-Tencent":[202],"100K":[203],"dataset":[204],"demonstrate":[205],"performance":[207,220],"Compared":[211],"state-of-the-art":[214],"methods,":[215],"achieves":[218],"competitive":[219],"(92.16&#x0025;":[221],"mAP)":[222],"(<inline-formula>":[227],"<tex-math":[228,237],"notation=\"LaTeX\">$6.49M":[229],"$":[230,239],"</tex-math></inline-formula>).":[231],"time":[234],"<inline-formula>":[236],"notation=\"LaTeX\">$0.150s":[238],"</tex-math></inline-formula>":[240],"per":[241],"frame,":[242],"which":[244],"speed":[246],"increased":[248],"3":[250],"times":[251],"compared":[252],"existing":[254],"methods.":[255]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}