{"id":"https://openalex.org/W2962753897","doi":"https://doi.org/10.1109/ijcnn.2019.8852086","title":"Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images","display_name":"Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2962753897","doi":"https://doi.org/10.1109/ijcnn.2019.8852086","mag":"2962753897"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852086","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1907.09679","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Lucas Tabelini Torres","orcid":null},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Lucas Tabelini Torres","raw_affiliation_strings":["Universidade Federal do Esp\u00edrito Santo, Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal do Esp\u00edrito Santo, Brazil","institution_ids":["https://openalex.org/I51235708"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Thiago M. Paixao","orcid":null},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Thiago M. Paixao","raw_affiliation_strings":["Universidade Federal do Esp\u00edrito Santo, Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal do Esp\u00edrito Santo, Brazil","institution_ids":["https://openalex.org/I51235708"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Rodrigo F. Berriel","orcid":null},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rodrigo F. Berriel","raw_affiliation_strings":["Universidade Federal do Esp\u00edrito Santo, Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal do Esp\u00edrito Santo, Brazil","institution_ids":["https://openalex.org/I51235708"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Alberto F. De Souza","orcid":null},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Alberto F. De Souza","raw_affiliation_strings":["Universidade Federal do Esp\u00edrito Santo, Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal do Esp\u00edrito Santo, Brazil","institution_ids":["https://openalex.org/I51235708"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Claudine Badue","orcid":null},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Claudine Badue","raw_affiliation_strings":["Universidade Federal do Esp\u00edrito Santo, Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal do Esp\u00edrito Santo, Brazil","institution_ids":["https://openalex.org/I51235708"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Nicu Sebe","orcid":null},"institutions":[{"id":"https://openalex.org/I193223587","display_name":"University of Trento","ror":"https://ror.org/05trd4x28","country_code":"IT","type":"education","lineage":["https://openalex.org/I193223587"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Nicu Sebe","raw_affiliation_strings":["University of Trento, Italy"],"affiliations":[{"raw_affiliation_string":"University of Trento, Italy","institution_ids":["https://openalex.org/I193223587"]}]},{"author_position":"last","author":{"id":null,"display_name":"Thiago Oliveira-Santos","orcid":null},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Thiago Oliveira-Santos","raw_affiliation_strings":["Universidade Federal do Esp\u00edrito Santo, Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal do Esp\u00edrito Santo, Brazil","institution_ids":["https://openalex.org/I51235708"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I51235708"],"apc_list":null,"apc_paid":null,"fwci":0.8174,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.77193898,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.996399998664856,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9897000193595886,"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","display_name":"Traffic sign","score":0.6894000172615051},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5741999745368958},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.565500020980835},{"id":"https://openalex.org/keywords/template","display_name":"Template","score":0.5625},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4909999966621399},{"id":"https://openalex.org/keywords/sign","display_name":"Sign (mathematics)","score":0.4212000072002411},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4187999963760376},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.412200003862381},{"id":"https://openalex.org/keywords/traffic-sign-recognition","display_name":"Traffic sign recognition","score":0.40639999508857727}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7890999913215637},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7055000066757202},{"id":"https://openalex.org/C2983860417","wikidata":"https://www.wikidata.org/wiki/Q170285","display_name":"Traffic sign","level":3,"score":0.6894000172615051},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5741999745368958},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.565500020980835},{"id":"https://openalex.org/C82714645","wikidata":"https://www.wikidata.org/wiki/Q438331","display_name":"Template","level":2,"score":0.5625},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5271000266075134},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4909999966621399},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.4212000072002411},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4187999963760376},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.412200003862381},{"id":"https://openalex.org/C6528762","wikidata":"https://www.wikidata.org/wiki/Q1574298","display_name":"Traffic sign recognition","level":4,"score":0.40639999508857727},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.39890000224113464},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.37779998779296875},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32989999651908875},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.32510000467300415},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.32019999623298645},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27140000462532043},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2502000033855438}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852086","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1907.09679","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1907.09679","pdf_url":"https://arxiv.org/pdf/1907.09679","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:iris.unitn.it:11572/250738","is_oa":true,"landing_page_url":"http://hdl.handle.net/11572/250738","pdf_url":null,"source":{"id":"https://openalex.org/S4306401913","display_name":"Institutional Research Information System (Universit\u00e0 degli Studi di Trento)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I193223587","host_organization_name":"University of Trento","host_organization_lineage":["https://openalex.org/I193223587"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1907.09679","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1907.09679","pdf_url":"https://arxiv.org/pdf/1907.09679","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1994488211","https://openalex.org/W2002427601","https://openalex.org/W2131171972","https://openalex.org/W2148308609","https://openalex.org/W2150581781","https://openalex.org/W2440599146","https://openalex.org/W2496066288","https://openalex.org/W2727957049","https://openalex.org/W2737202447","https://openalex.org/W2748570250","https://openalex.org/W2809503262","https://openalex.org/W2887850625","https://openalex.org/W2896644620","https://openalex.org/W2897847835","https://openalex.org/W2951007713","https://openalex.org/W2963296620","https://openalex.org/W2964309882","https://openalex.org/W6639102338","https://openalex.org/W6677954473","https://openalex.org/W6684191040","https://openalex.org/W6686583229","https://openalex.org/W6723543151","https://openalex.org/W6744066916","https://openalex.org/W6748561909","https://openalex.org/W6756851690","https://openalex.org/W6758283773","https://openalex.org/W6763781849"],"related_works":[],"abstract_inverted_index":{"Deep":[0],"learning":[1],"has":[2],"been":[3],"successfully":[4],"applied":[5],"to":[6,10,82,141,159,190],"several":[7],"problems":[8],"related":[9],"autonomous":[11,49],"driving.":[12],"Often,":[13],"these":[14,97],"solutions":[15],"rely":[16],"on":[17,173,181],"large":[18],"networks":[19],"that":[20,77,88,113,212],"require":[21],"databases":[22],"of":[23,27,38,128,133,145,148,165,179,201,236],"real":[24,31,123],"image":[25,124],"samples":[26],"the":[28,48,126,134,143,146,163,185,228,233,237],"problem":[29,94,224],"(i.e.,":[30],"world)":[32],"for":[33,162,227,240],"proper":[34],"training.":[35],"The":[36,152,208],"acquisition":[37],"such":[39],"real-world":[40],"data":[41,75,219,226],"sets":[42],"is":[43,56,64,72,92,157,188,231],"not":[44,57],"always":[45],"possible":[46],"in":[47,68,95,232],"driving":[50],"context,":[51],"and":[52,130,199,205,222],"sometimes":[53],"their":[54],"annotation":[55],"feasible":[58],"(e.g.,":[59],"takes":[60],"too":[61,65],"long":[62],"or":[63],"expensive).":[66],"Moreover,":[67],"many":[69],"tasks,":[70],"there":[71],"an":[73,195],"intrinsic":[74],"imbalance":[76],"most":[78],"learning-based":[79],"methods":[80,221],"struggle":[81],"cope":[83],"with.":[84],"It":[85],"turns":[86],"out":[87],"traffic":[89,135,150,175,192],"sign":[90],"detection":[91],"a":[93,108,149,166],"which":[96,230],"three":[98],"issues":[99],"are":[100],"seen":[101],"altogether.":[102],"In":[103,183],"this":[104],"work,":[105],"we":[106],"propose":[107],"novel":[109],"database":[110,156],"generation":[111,220],"method":[112,187],"requires":[114,121],"only":[115],"(i)":[116],"arbitrary":[117],"natural":[118],"images,":[119],"i.e.,":[120,137],"no":[122],"from":[125],"domain":[127,225],"interest,":[129],"(ii)":[131],"templates":[132,138],"signs,":[136,176],"synthetically":[139],"created":[140],"illustrate":[142],"appearance":[144],"category":[147],"sign.":[151],"effortlessly":[153],"generated":[154],"training":[155,164],"shown":[158],"be":[160,215],"effective":[161],"deep":[167,241],"detector":[168],"(such":[169],"as":[170],"Faster":[171],"R-CNN)":[172],"German":[174],"achieving":[177],"95.66%":[178],"mAP":[180],"average.":[182],"addition,":[184],"proposed":[186],"able":[189],"detect":[191],"signs":[193],"with":[194,217],"average":[196],"precision,":[197],"recall":[198],"F1-score":[200],"about":[202],"94%,":[203],"91%":[204],"93%,":[206],"respectively.":[207],"experiments":[209],"surprisingly":[210],"show":[211],"detectors":[213],"can":[214],"trained":[216],"simple":[218],"without":[223],"background,":[229],"opposite":[234],"direction":[235],"common":[238],"sense":[239],"learning.":[242]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2019-07-30T00:00:00"}
