{"id":"https://openalex.org/W2512944926","doi":"https://doi.org/10.1109/ivs.2016.7535408","title":"DeepTLR: A single deep convolutional network for detection and classification of traffic lights","display_name":"DeepTLR: A single deep convolutional network for detection and classification of traffic lights","publication_year":2016,"publication_date":"2016-06-01","ids":{"openalex":"https://openalex.org/W2512944926","doi":"https://doi.org/10.1109/ivs.2016.7535408","mag":"2512944926"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2016.7535408","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2016.7535408","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Intelligent Vehicles Symposium (IV)","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/A5103159140","display_name":"Michael Weber","orcid":"https://orcid.org/0000-0002-2692-5568"},"institutions":[{"id":"https://openalex.org/I143379178","display_name":"FZI Research Center for Information Technology","ror":"https://ror.org/04kdh6x72","country_code":"DE","type":"nonprofit","lineage":["https://openalex.org/I143379178"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Michael Weber","raw_affiliation_strings":["FZI Research Center for Information Technology, Karlsruhe, Germany"],"affiliations":[{"raw_affiliation_string":"FZI Research Center for Information Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I143379178"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046518573","display_name":"Peter Wolf","orcid":"https://orcid.org/0000-0003-3616-9038"},"institutions":[{"id":"https://openalex.org/I143379178","display_name":"FZI Research Center for Information Technology","ror":"https://ror.org/04kdh6x72","country_code":"DE","type":"nonprofit","lineage":["https://openalex.org/I143379178"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Peter Wolf","raw_affiliation_strings":["FZI Research Center for Information Technology, Karlsruhe, Germany"],"affiliations":[{"raw_affiliation_string":"FZI Research Center for Information Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I143379178"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060028048","display_name":"J. Marius Z\u00f6llner","orcid":"https://orcid.org/0000-0001-6190-7202"},"institutions":[{"id":"https://openalex.org/I143379178","display_name":"FZI Research Center for Information Technology","ror":"https://ror.org/04kdh6x72","country_code":"DE","type":"nonprofit","lineage":["https://openalex.org/I143379178"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"J. Marius Zollner","raw_affiliation_strings":["FZI Research Center for Information Technology, Karlsruhe, Germany"],"affiliations":[{"raw_affiliation_string":"FZI Research Center for Information Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I143379178"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103159140"],"corresponding_institution_ids":["https://openalex.org/I143379178"],"apc_list":null,"apc_paid":null,"fwci":5.8929,"has_fulltext":false,"cited_by_count":99,"citation_normalized_percentile":{"value":0.97643679,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"342","last_page":"348"},"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9988999962806702,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.818474531173706},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7710617780685425},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6965746879577637},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.6911039352416992},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.6852875351905823},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6115326285362244},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5735517740249634},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.5369254350662231},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5313981771469116},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5278962254524231},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.45801061391830444},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40878328680992126},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.32907161116600037}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.818474531173706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7710617780685425},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6965746879577637},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.6911039352416992},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.6852875351905823},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6115326285362244},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5735517740249634},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.5369254350662231},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5313981771469116},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5278962254524231},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.45801061391830444},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40878328680992126},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32907161116600037},{"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/ivs.2016.7535408","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2016.7535408","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5199999809265137,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W198559847","https://openalex.org/W1585377561","https://openalex.org/W1903029394","https://openalex.org/W1927391347","https://openalex.org/W2021383150","https://openalex.org/W2059050813","https://openalex.org/W2060602100","https://openalex.org/W2105529173","https://openalex.org/W2112796928","https://openalex.org/W2114281890","https://openalex.org/W2117539524","https://openalex.org/W2147800946","https://openalex.org/W2155893237","https://openalex.org/W2163605009","https://openalex.org/W2217782816","https://openalex.org/W2292685297","https://openalex.org/W2395611524","https://openalex.org/W2952020226","https://openalex.org/W2963542991","https://openalex.org/W3210232381","https://openalex.org/W4297666078","https://openalex.org/W6608056700","https://openalex.org/W6629368666","https://openalex.org/W6635292102","https://openalex.org/W6640054144","https://openalex.org/W6677651945","https://openalex.org/W6684191040","https://openalex.org/W6803376173"],"related_works":["https://openalex.org/W3192357901","https://openalex.org/W3036286480","https://openalex.org/W2387360586","https://openalex.org/W4287027631","https://openalex.org/W4237171675","https://openalex.org/W2952736415","https://openalex.org/W3209723314","https://openalex.org/W3205398323","https://openalex.org/W2883297582","https://openalex.org/W2962677013"],"abstract_inverted_index":{"Reliable":[0],"real-time":[1,38,132],"detection":[2,39,70],"of":[3,13,42,104,116],"traffic":[4,43,65,85],"lights":[5,86],"is":[6,71,81,96,124],"a":[7,24,34,52,110],"major":[8],"concern":[9],"for":[10,37,131],"the":[11,69,88,105],"task":[12],"autonomous":[14],"driving.":[15],"As":[16],"deep":[17,54],"convolutional":[18,55],"networks":[19],"have":[20],"proven":[21],"to":[22,83,126],"be":[23],"powerful":[25],"tool":[26],"in":[27],"visual":[28],"object":[29],"detection,":[30],"we":[31],"propose":[32],"DeepTLR,":[33],"camera-based":[35],"system":[36],"and":[40,46,108],"classification":[41,48],"lights.":[44],"Detection":[45],"state":[47],"are":[49],"realized":[50],"using":[51,77],"single":[53],"network.":[56],"DeepTLR":[57],"does":[58],"not":[59],"use":[60],"any":[61,93],"prior":[62],"knowledge":[63],"about":[64],"light":[66],"locations.":[67],"Also":[68],"executed":[72],"frame":[73,75],"by":[74,98],"without":[76,92],"temporal":[78],"information.":[79],"It":[80],"able":[82,125],"detect":[84],"on":[87,114,128],"whole":[89],"camera":[90],"image":[91,107],"presegmentation.":[94],"This":[95],"achieved":[97],"classifying":[99],"each":[100,117],"fine-grained":[101],"pixel":[102],"region":[103],"input":[106],"performing":[109],"bounding":[111],"box":[112],"regression":[113],"regions":[115],"class.":[118],"We":[119],"show":[120],"that":[121],"our":[122],"algorithm":[123],"run":[127],"frame-rates":[129],"required":[130],"applications":[133],"while":[134],"reaching":[135],"notable":[136],"results.":[137]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":17},{"year":2019,"cited_by_count":14},{"year":2018,"cited_by_count":10},{"year":2017,"cited_by_count":10}],"updated_date":"2026-02-08T09:19:03.324500","created_date":"2025-10-10T00:00:00"}