{"id":"https://openalex.org/W7160648279","doi":"https://doi.org/10.1145/3796315.3796360","title":"Traffic Signal Image Recognition with Lightweight Machine Learning Model","display_name":"Traffic Signal Image Recognition with Lightweight Machine Learning Model","publication_year":2026,"publication_date":"2026-01-21","ids":{"openalex":"https://openalex.org/W7160648279","doi":"https://doi.org/10.1145/3796315.3796360"},"language":null,"primary_location":{"id":"doi:10.1145/3796315.3796360","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3796315.3796360","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 9th International Conference on Software Engineering and Information Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3796315.3796360","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059321688","display_name":"Yichao Wu","orcid":"https://orcid.org/0000-0003-4496-9959"},"institutions":[{"id":"https://openalex.org/I118292597","display_name":"National Taipei University of Technology","ror":"https://ror.org/00cn92c09","country_code":"TW","type":"education","lineage":["https://openalex.org/I118292597"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yi-Chao Wu","raw_affiliation_strings":["Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan"],"raw_orcid":"https://orcid.org/0009-0002-2386-6117","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan","institution_ids":["https://openalex.org/I118292597"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135676457","display_name":"Zong-Yan Lin","orcid":"https://orcid.org/0009-0005-0168-2503"},"institutions":[{"id":"https://openalex.org/I75357094","display_name":"National Yunlin University of Science and Technology","ror":"https://ror.org/04qkq2m54","country_code":"TW","type":"education","lineage":["https://openalex.org/I75357094"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Zong-Yan Lin","raw_affiliation_strings":["Department of Electronic Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan"],"raw_orcid":"https://orcid.org/0009-0005-0168-2503","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan","institution_ids":["https://openalex.org/I75357094"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135720045","display_name":"Yu-Rong Ciou","orcid":"https://orcid.org/0009-0002-6546-3658"},"institutions":[{"id":"https://openalex.org/I75357094","display_name":"National Yunlin University of Science and Technology","ror":"https://ror.org/04qkq2m54","country_code":"TW","type":"education","lineage":["https://openalex.org/I75357094"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yu-Rong Ciou","raw_affiliation_strings":["Department of Electronic Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan"],"raw_orcid":"https://orcid.org/0009-0002-6546-3658","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan","institution_ids":["https://openalex.org/I75357094"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5135684904","display_name":"Jing-Xiang Xu","orcid":"https://orcid.org/0009-0009-6399-8991"},"institutions":[{"id":"https://openalex.org/I75357094","display_name":"National Yunlin University of Science and Technology","ror":"https://ror.org/04qkq2m54","country_code":"TW","type":"education","lineage":["https://openalex.org/I75357094"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jing-Xiang Xu","raw_affiliation_strings":["Department of Electronic Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan"],"raw_orcid":"https://orcid.org/0009-0009-6399-8991","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan","institution_ids":["https://openalex.org/I75357094"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.7188229,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"328","last_page":"334"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.4099999964237213,"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":0.4099999964237213,"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/T12406","display_name":"IoT and GPS-based Vehicle Safety Systems","score":0.05550000071525574,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.03889999911189079,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/traffic-sign-recognition","display_name":"Traffic sign recognition","score":0.7358999848365784},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4984999895095825},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4643999934196472},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.45170000195503235},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.444599986076355},{"id":"https://openalex.org/keywords/warning-system","display_name":"Warning system","score":0.4269999861717224},{"id":"https://openalex.org/keywords/traffic-sign","display_name":"Traffic sign","score":0.41600000858306885}],"concepts":[{"id":"https://openalex.org/C6528762","wikidata":"https://www.wikidata.org/wiki/Q1574298","display_name":"Traffic sign recognition","level":4,"score":0.7358999848365784},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6315000057220459},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6212999820709229},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4984999895095825},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4643999934196472},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4569999873638153},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.45170000195503235},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.444599986076355},{"id":"https://openalex.org/C29825287","wikidata":"https://www.wikidata.org/wiki/Q1427940","display_name":"Warning system","level":2,"score":0.4269999861717224},{"id":"https://openalex.org/C2983860417","wikidata":"https://www.wikidata.org/wiki/Q170285","display_name":"Traffic sign","level":3,"score":0.41600000858306885},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.38420000672340393},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3474999964237213},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.323199987411499},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.3190999925136566},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C183469790","wikidata":"https://www.wikidata.org/wiki/Q333501","display_name":"Crash","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.26170000433921814}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3796315.3796360","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3796315.3796360","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 9th International Conference on Software Engineering and Information Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3796315.3796360","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3796315.3796360","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 9th International Conference on Software Engineering and Information Management","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5913772583007812,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W4398222715","https://openalex.org/W4400625252","https://openalex.org/W4400876994","https://openalex.org/W4402135332","https://openalex.org/W4402751321","https://openalex.org/W4402985759","https://openalex.org/W4408226494","https://openalex.org/W4409691894"],"related_works":[],"abstract_inverted_index":{"In":[0,145],"paper":[1,45,99,220],"survey,":[2],"it":[3],"showed":[4],"that":[5,25,73,130],"the":[6,140,146,150,164,180,243,248,255,270,285],"vast":[7],"majority":[8],"of":[9,142],"traffic":[10,18,38,107,118,143,196,303],"accidents":[11,304],"are":[12,161],"related":[13],"to":[14,16,27,78,212,296],"failure":[15,26],"obey":[17,28],"signs.":[19,51,310],"Literature":[20],"analysis":[21],"and":[22,30,49,66,81,90,122,136,154,166,183,203,217,238,264,292,308],"research":[23],"revealed":[24],"warning":[29,48,65,153,202,307],"prohibition":[31,50,67,155,204,309],"signs":[32,68,156,205],"is":[33,69,76,175,245],"also":[34,94],"significantly":[35],"associated":[36],"with":[37,54,228,247,275],"accidents.":[39,144],"With":[40],"this":[41,44,74,98,188,219,268],"in":[42,111,187,267],"mind,":[43],"focuses":[46],"on":[47,126,177,284],"Combining":[52],"AI":[53,59,117],"information":[55],"training":[56,165,185],"technology,":[57],"an":[58,222,232,252],"image":[60,85,109,120,198],"recognition":[61,86,110,121,199],"system":[62,75],"suitable":[63,200],"for":[64,106,195,201,301],"proposed.":[70],"To":[71],"ensure":[72],"applicable":[77],"both":[79],"automobiles":[80],"motorcycles,":[82],"achieving":[83],"accurate":[84],"while":[87,93],"minimizing":[88],"footprint":[89],"power":[91,134,215],"consumption,":[92,135,216],"considering":[95],"hardware":[96],"costs,":[97],"proposes":[100],"a":[101,190,235,239],"lightweight":[102,171,191],"machine":[103,172,192],"learning":[104,173,193],"model":[105,115,174,184,194,244,261],"sign":[108,119,197],"edge":[112,127,209,224],"devices.":[113],"This":[114],"achieves":[116],"can":[123],"be":[124],"implemented":[125],"computing":[128,210,225],"devices":[129],"require":[131],"minimal":[132],"footprint,":[133,214],"cost,":[137,218],"thereby":[138],"reducing":[139,302],"incidence":[141],"data":[147,168,181,256],"collection":[148],"phase,":[149],"top":[151],"ten":[152],"ranked":[157],"by":[158,306],"accident":[159],"causes":[160],"used":[162],"as":[163],"test":[167],"sets.":[169],"The":[170],"based":[176,283],"TensorFlow.":[178],"Through":[179,254],"preprocessing":[182],"proposed":[186,282],"paper,":[189,269],"was":[206,281],"developed.":[207],"For":[208],"implementation,":[211],"minimize":[213],"uses":[221],"ESP32-CAM":[223],"development":[226,249],"board":[227,250],"built-in":[229],"Wi-Fi,":[230],"Bluetooth,":[231],"OV2640":[233],"camera,":[234],"TF":[236],"card,":[237],"flash":[240],"module.":[241],"Finally,":[242],"integrated":[246],"through":[251],"API.":[253],"collection,":[257],"preprocessing,":[258],"feature":[259,298],"extraction,":[260],"building,":[262],"training,":[263],"validation":[265],"described":[266],"Traffic":[271],"Signal":[272],"Image":[273],"Recognition":[274],"Lightweight":[276],"Machine":[277],"Learning":[278],"Model,":[279],"TRLM,":[280],"Single":[286],"Shot":[287],"MultiBox":[288],"Detector":[289],"MobileNet":[290],"(SSD-MobileNet)":[291],"Edge":[293],"Impulse":[294],"TinyML":[295],"enhance":[297],"fusion":[299],"capabilities":[300],"caused":[305]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-09T00:00:00"}