{"id":"https://openalex.org/W4308821063","doi":"https://doi.org/10.23919/spa53010.2022.9927864","title":"Traffic Sign Detection With Event Cameras and DCNN","display_name":"Traffic Sign Detection With Event Cameras and DCNN","publication_year":2022,"publication_date":"2022-09-21","ids":{"openalex":"https://openalex.org/W4308821063","doi":"https://doi.org/10.23919/spa53010.2022.9927864"},"language":"en","primary_location":{"id":"doi:10.23919/spa53010.2022.9927864","is_oa":false,"landing_page_url":"https://doi.org/10.23919/spa53010.2022.9927864","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","raw_type":"proceedings-article"},"type":"preprint","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/A5041564501","display_name":"Piotr Wzorek","orcid":"https://orcid.org/0000-0003-3885-600X"},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Piotr Wzorek","raw_affiliation_strings":["Embedded Vision Systems Group, AGH University of Science and Technology,Computer Vision Laboratory,Department of Automatic Control and Robotics,Kraków,Poland"],"affiliations":[{"raw_affiliation_string":"Embedded Vision Systems Group, AGH University of Science and Technology,Computer Vision Laboratory,Department of Automatic Control and Robotics,Kraków,Poland","institution_ids":["https://openalex.org/I686019"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005086061","display_name":"Tomasz Kryjak","orcid":"https://orcid.org/0000-0001-6798-4444"},"institutions":[{"id":"https://openalex.org/I119004910","display_name":"Silesian University of Technology","ror":"https://ror.org/02dyjk442","country_code":"PL","type":"education","lineage":["https://openalex.org/I119004910"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Tomasz Kryjak","raw_affiliation_strings":["Silesian University of Technology,Department of Digital Systems,Gliwice,Poland","Department of Digital Systems, Silesian University of Technology, Gliwice, Poland"],"affiliations":[{"raw_affiliation_string":"Silesian University of Technology,Department of Digital Systems,Gliwice,Poland","institution_ids":["https://openalex.org/I119004910"]},{"raw_affiliation_string":"Department of Digital Systems, Silesian University of Technology, Gliwice, Poland","institution_ids":["https://openalex.org/I119004910"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5041564501"],"corresponding_institution_ids":["https://openalex.org/I686019"],"apc_list":null,"apc_paid":null,"fwci":1.18411392,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.75268489,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"86","last_page":"91"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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":0.973800003528595,"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":0.8039673566818237},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6763351559638977},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6690952181816101},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6289563775062561},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6182748079299927},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.589877188205719},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.5774074196815491},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5433358550071716},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5312235951423645},{"id":"https://openalex.org/keywords/high-dynamic-range","display_name":"High dynamic range","score":0.48266035318374634},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44100168347358704},{"id":"https://openalex.org/keywords/traffic-sign-recognition","display_name":"Traffic sign recognition","score":0.42905205488204956},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.42024803161621094},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4121389389038086},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.38171079754829407},{"id":"https://openalex.org/keywords/traffic-sign","display_name":"Traffic sign","score":0.3025071918964386},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.24532437324523926},{"id":"https://openalex.org/keywords/sign","display_name":"Sign (mathematics)","score":0.23925334215164185},{"id":"https://openalex.org/keywords/dynamic-range","display_name":"Dynamic range","score":0.20917439460754395}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8039673566818237},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6763351559638977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6690952181816101},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6289563775062561},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6182748079299927},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.589877188205719},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.5774074196815491},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5433358550071716},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5312235951423645},{"id":"https://openalex.org/C2780056265","wikidata":"https://www.wikidata.org/wiki/Q106239881","display_name":"High dynamic range","level":3,"score":0.48266035318374634},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44100168347358704},{"id":"https://openalex.org/C6528762","wikidata":"https://www.wikidata.org/wiki/Q1574298","display_name":"Traffic sign recognition","level":4,"score":0.42905205488204956},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.42024803161621094},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4121389389038086},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.38171079754829407},{"id":"https://openalex.org/C2983860417","wikidata":"https://www.wikidata.org/wiki/Q170285","display_name":"Traffic sign","level":3,"score":0.3025071918964386},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.24532437324523926},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.23925334215164185},{"id":"https://openalex.org/C87133666","wikidata":"https://www.wikidata.org/wiki/Q1161699","display_name":"Dynamic range","level":2,"score":0.20917439460754395},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/spa53010.2022.9927864","is_oa":false,"landing_page_url":"https://doi.org/10.23919/spa53010.2022.9927864","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Partnerships for the goals","score":0.46000000834465027,"id":"https://metadata.un.org/sdg/17"}],"awards":[{"id":"https://openalex.org/G5006570393","display_name":null,"funder_award_id":"16.16.120.773","funder_id":"https://openalex.org/F4320324664","funder_display_name":"Akademia G\u00f3rniczo-Hutnicza im. Stanislawa Staszica"}],"funders":[{"id":"https://openalex.org/F4320324664","display_name":"Akademia G\u00f3rniczo-Hutnicza im. Stanislawa Staszica","ror":"https://ror.org/00bas1c41"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1967377294","https://openalex.org/W2067713319","https://openalex.org/W2769320958","https://openalex.org/W2938463073","https://openalex.org/W2964981172","https://openalex.org/W3010556452","https://openalex.org/W3011302802","https://openalex.org/W3018757597","https://openalex.org/W3040838455","https://openalex.org/W3090117981","https://openalex.org/W3090440982","https://openalex.org/W3139885827","https://openalex.org/W3202344666","https://openalex.org/W4300167295","https://openalex.org/W6639102338","https://openalex.org/W6745178058","https://openalex.org/W6761311290","https://openalex.org/W6777046832","https://openalex.org/W6783508657"],"related_works":["https://openalex.org/W4382897155","https://openalex.org/W4379231512","https://openalex.org/W4283820116","https://openalex.org/W4378699879","https://openalex.org/W3128164723","https://openalex.org/W3215426395","https://openalex.org/W4286647459","https://openalex.org/W2899819381","https://openalex.org/W2557202782","https://openalex.org/W2772251146"],"abstract_inverted_index":{"In":[0,61,161],"recent":[1],"years,":[2],"event":[3,91,93,95,186],"cameras":[4,187],"(DVS":[5],"-":[6,43],"Dynamic":[7],"Vision":[8],"Sensors)":[9],"have":[10],"been":[11],"used":[12],"in":[13,39,49,134,188,196],"vision":[14],"systems":[15,56],"as":[16,123,192],"an":[17,174],"alternative":[18],"or":[19,195],"supplement":[20],"to":[21,74,151],"traditional":[22],"cameras.":[23,202],"They":[24],"are":[25,46],"characterised":[26,172],"by":[27,173],"high":[28,31],"dynamic":[29],"range,":[30],"temporal":[32],"resolution,":[33],"low":[34],"latency,":[35],"and":[36,58,97,103],"reliable":[37],"performance":[38],"limited":[40],"lighting":[41],"conditions":[42],"parameters":[44],"that":[45],"particularly":[47],"important":[48],"the":[50,75,90,98,117,135,146,163,166,183],"context":[51],"of":[52,78,89,137,142,145,158,176,185],"advanced":[53],"driver":[54],"assistance":[55],"(ADAS)":[57],"self-driving":[59],"cars.":[60],"this":[62,83],"work,":[63],"we":[64,85,129],"test":[65],"whether":[66],"these":[67],"rather":[68],"novel":[69],"sensors":[70,194],"can":[71],"be":[72],"applied":[73],"popular":[76],"task":[77],"traffic":[79],"sign":[80],"detection.":[81],"To":[82],"end,":[84],"analyse":[86],"different":[87],"representations":[88,148],"data:":[92],"frame,":[94],"frequency,":[96],"exponentially":[99],"decaying":[100],"time":[101],"surface,":[102],"apply":[104],"video":[105],"frame":[106],"reconstruction":[107],"using":[108],"a":[109,124,131,143,153],"deep":[110,118],"neural":[111,120],"network":[112,121],"called":[113],"FireNet.":[114],"We":[115],"use":[116,141],"convolutional":[119],"YOLOv4":[122],"detector.":[125],"For":[126],"particular":[127],"representations,":[128],"obtain":[130,152],"detection":[132],"accuracy":[133,157,175],"range":[136],"86.9-88.9%":[138],"mAP@0.5.":[139,160,178],"The":[140,179],"fusion":[144],"considered":[147],"allows":[149],"us":[150],"detector":[154,164],"with":[155,169,199],"higher":[156],"89.9%":[159],"comparison,":[162],"for":[165],"frames":[167],"reconstructed":[168],"FireNet":[170],"is":[171],"72.67%":[177],"results":[180],"obtained":[181],"illustrate":[182],"potential":[184],"automotive":[189],"applications,":[190],"either":[191],"standalone":[193],"close":[197],"cooperation":[198],"typical":[200],"frame-based":[201]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":9}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}