{"id":"https://openalex.org/W2195295035","doi":"https://doi.org/10.1117/12.2228582","title":"Toward an optimal convolutional neural network for traffic sign recognition","display_name":"Toward an optimal convolutional neural network for traffic sign recognition","publication_year":2015,"publication_date":"2015-12-08","ids":{"openalex":"https://openalex.org/W2195295035","doi":"https://doi.org/10.1117/12.2228582","mag":"2195295035"},"language":"en","primary_location":{"id":"doi:10.1117/12.2228582","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2228582","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-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/A5047056785","display_name":"Hamed Habibi Aghdam","orcid":"https://orcid.org/0000-0002-4881-9694"},"institutions":[{"id":"https://openalex.org/I55952717","display_name":"Universidad Rovira i Virgili","ror":"https://ror.org/00g5sqv46","country_code":"ES","type":"education","lineage":["https://openalex.org/I55952717"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Hamed Habibi Aghdam","raw_affiliation_strings":["Univ. Rovira i Virgili (Spain)"],"affiliations":[{"raw_affiliation_string":"Univ. Rovira i Virgili (Spain)","institution_ids":["https://openalex.org/I55952717"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103256130","display_name":"Elnaz Jahani Heravi","orcid":"https://orcid.org/0000-0003-3641-8225"},"institutions":[{"id":"https://openalex.org/I55952717","display_name":"Universidad Rovira i Virgili","ror":"https://ror.org/00g5sqv46","country_code":"ES","type":"education","lineage":["https://openalex.org/I55952717"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Elnaz Jahani Heravi","raw_affiliation_strings":["Univ. Rovira i Virgili (Spain)"],"affiliations":[{"raw_affiliation_string":"Univ. Rovira i Virgili (Spain)","institution_ids":["https://openalex.org/I55952717"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004665770","display_name":"Dom\u00e8nec Puig","orcid":"https://orcid.org/0000-0002-0562-4205"},"institutions":[{"id":"https://openalex.org/I55952717","display_name":"Universidad Rovira i Virgili","ror":"https://ror.org/00g5sqv46","country_code":"ES","type":"education","lineage":["https://openalex.org/I55952717"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Domenec Puig","raw_affiliation_strings":["Univ. Rovira i Virgili (Spain)"],"affiliations":[{"raw_affiliation_string":"Univ. Rovira i Virgili (Spain)","institution_ids":["https://openalex.org/I55952717"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5047056785"],"corresponding_institution_ids":["https://openalex.org/I55952717"],"apc_list":null,"apc_paid":null,"fwci":0.5523,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.75143211,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"9875","issue":null,"first_page":"98750K","last_page":"98750K"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9976999759674072,"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":0.9976999759674072,"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":0.9944000244140625,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9854000210762024,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/activation-function","display_name":"Activation function","score":0.840576708316803},{"id":"https://openalex.org/keywords/sigmoid-function","display_name":"Sigmoid function","score":0.8099640607833862},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7543935775756836},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7488354444503784},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6931349039077759},{"id":"https://openalex.org/keywords/traffic-sign-recognition","display_name":"Traffic sign recognition","score":0.575757622718811},{"id":"https://openalex.org/keywords/hyperbolic-function","display_name":"Hyperbolic function","score":0.5176516175270081},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49085885286331177},{"id":"https://openalex.org/keywords/sign","display_name":"Sign (mathematics)","score":0.4850460886955261},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4476569592952728},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35287052392959595},{"id":"https://openalex.org/keywords/traffic-sign","display_name":"Traffic sign","score":0.3276069760322571},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32532209157943726},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11537998914718628}],"concepts":[{"id":"https://openalex.org/C38365724","wikidata":"https://www.wikidata.org/wiki/Q4677469","display_name":"Activation function","level":3,"score":0.840576708316803},{"id":"https://openalex.org/C81388566","wikidata":"https://www.wikidata.org/wiki/Q526668","display_name":"Sigmoid function","level":3,"score":0.8099640607833862},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7543935775756836},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7488354444503784},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6931349039077759},{"id":"https://openalex.org/C6528762","wikidata":"https://www.wikidata.org/wiki/Q1574298","display_name":"Traffic sign recognition","level":4,"score":0.575757622718811},{"id":"https://openalex.org/C92047909","wikidata":"https://www.wikidata.org/wiki/Q204034","display_name":"Hyperbolic function","level":2,"score":0.5176516175270081},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49085885286331177},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.4850460886955261},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4476569592952728},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35287052392959595},{"id":"https://openalex.org/C2983860417","wikidata":"https://www.wikidata.org/wiki/Q170285","display_name":"Traffic sign","level":3,"score":0.3276069760322571},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32532209157943726},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11537998914718628},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2228582","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2228582","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321505","display_name":"Generalitat de Catalunya","ror":"https://ror.org/01bg62x04"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1576278180","https://openalex.org/W1686810756","https://openalex.org/W1904365287","https://openalex.org/W2067713319","https://openalex.org/W2097117768","https://openalex.org/W2108069432","https://openalex.org/W2125085157","https://openalex.org/W2163605009","https://openalex.org/W2405108485","https://openalex.org/W6637373629","https://openalex.org/W6638444622","https://openalex.org/W6640036494","https://openalex.org/W6674914833","https://openalex.org/W6678644335","https://openalex.org/W6684191040","https://openalex.org/W6713085681"],"related_works":["https://openalex.org/W4211198594","https://openalex.org/W4295036712","https://openalex.org/W2811324119","https://openalex.org/W3216434047","https://openalex.org/W4385451479","https://openalex.org/W3110577345","https://openalex.org/W3175461337","https://openalex.org/W2032258609","https://openalex.org/W2906090557","https://openalex.org/W2889196975"],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Networks":[2],"(CNN)":[3],"beat":[4],"the":[5,15,18,58,61,68,81,92,97,107,125,132,142,150,158,162],"human":[6],"performance":[7],"on":[8,131,141],"German":[9],"Traffic":[10,134],"Sign":[11,135],"Benchmark":[12,136],"competition.":[13,163],"Both":[14],"winner":[16,159],"and":[17,41,64,100],"runner-up":[19],"teams":[20],"trained":[21],"CNNs":[22],"to":[23,90],"recognize":[24],"43":[25],"traffic":[26],"signs.":[27],"However,":[28],"both":[29],"networks":[30],"are":[31],"not":[32],"computationally":[33,120],"efficient":[34,123],"since":[35],"they":[36,42],"have":[37],"many":[38],"free":[39],"parameters":[40,62,154],"use":[43],"highly":[44],"computational":[45],"activation":[46,82,103],"functions.":[47,128],"In":[48],"this":[49],"paper,":[50],"we":[51],"propose":[52],"a":[53,87],"new":[54],"architecture":[55],"that":[56,84],"reduces":[57,149],"number":[59,152],"of":[60,153],"27%":[63],"22%":[65],"compared":[66,95,156],"with":[67,96,157],"two":[69,108,126],"networks.":[70],"Furthermore,":[71],"our":[72],"network":[73,160],"uses":[74],"Leaky":[75,110],"Rectified":[76],"Linear":[77],"Units":[78],"(ReLU)":[79],"as":[80],"function":[83],"only":[85,113],"needs":[86,112],"few":[88],"operations":[89],"produce":[91],"result.":[93],"Specifically,":[94],"hyperbolic":[98],"tangent":[99],"rectified":[101],"sigmoid":[102],"functions":[104],"utilized":[105],"in":[106,161],"networks,":[109],"ReLU":[111],"one":[114],"multiplication":[115],"operation":[116],"which":[117],"makes":[118],"it":[119,148],"much":[121],"more":[122],"than":[124],"other":[127],"Our":[129],"experiments":[130],"Gertman":[133],"dataset":[137],"shows":[138],"0:6%":[139],"improvement":[140],"best":[143],"reported":[144],"classification":[145],"accuracy":[146],"while":[147],"overall":[151],"85%":[155]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
