{"id":"https://openalex.org/W4361828897","doi":"https://doi.org/10.1145/3582177.3582178","title":"Performance Evaluation of Recent Object Detection Models for Traffic Safety Applications on Edge","display_name":"Performance Evaluation of Recent Object Detection Models for Traffic Safety Applications on Edge","publication_year":2023,"publication_date":"2023-01-13","ids":{"openalex":"https://openalex.org/W4361828897","doi":"https://doi.org/10.1145/3582177.3582178"},"language":"en","primary_location":{"id":"doi:10.1145/3582177.3582178","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3582177.3582178","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision","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/A5000348804","display_name":"Anilcan Bulut","orcid":"https://orcid.org/0000-0001-6051-7704"},"institutions":[{"id":"https://openalex.org/I74897591","display_name":"Marmara University","ror":"https://ror.org/02kswqa67","country_code":"TR","type":"education","lineage":["https://openalex.org/I74897591"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Anilcan Bulut","raw_affiliation_strings":["VeNIT Lab, Marmara University, Turkey"],"raw_orcid":"https://orcid.org/0000-0001-6051-7704","affiliations":[{"raw_affiliation_string":"VeNIT Lab, Marmara University, Turkey","institution_ids":["https://openalex.org/I74897591"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047074296","display_name":"Fatmanur Ozdemir","orcid":"https://orcid.org/0000-0002-7429-1344"},"institutions":[{"id":"https://openalex.org/I74897591","display_name":"Marmara University","ror":"https://ror.org/02kswqa67","country_code":"TR","type":"education","lineage":["https://openalex.org/I74897591"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Fatmanur Ozdemir","raw_affiliation_strings":["VeNIT Lab, Marmara University, Turkey"],"raw_orcid":"https://orcid.org/0000-0002-7429-1344","affiliations":[{"raw_affiliation_string":"VeNIT Lab, Marmara University, Turkey","institution_ids":["https://openalex.org/I74897591"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001258626","display_name":"Yavuz Selim Bostanci","orcid":"https://orcid.org/0000-0003-2799-1804"},"institutions":[{"id":"https://openalex.org/I74897591","display_name":"Marmara University","ror":"https://ror.org/02kswqa67","country_code":"TR","type":"education","lineage":["https://openalex.org/I74897591"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Yavuz Selim Bostanci","raw_affiliation_strings":["VeNIT Lab, Marmara University, Turkey"],"raw_orcid":"https://orcid.org/0000-0003-2799-1804","affiliations":[{"raw_affiliation_string":"VeNIT Lab, Marmara University, Turkey","institution_ids":["https://openalex.org/I74897591"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001688795","display_name":"M\u00fcjdat Soyt\u00fcrk","orcid":"https://orcid.org/0000-0003-2612-1460"},"institutions":[{"id":"https://openalex.org/I74897591","display_name":"Marmara University","ror":"https://ror.org/02kswqa67","country_code":"TR","type":"education","lineage":["https://openalex.org/I74897591"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Mujdat Soyturk","raw_affiliation_strings":["VeNIT Lab, Marmara University, Turkey"],"raw_orcid":"https://orcid.org/0000-0003-2612-1460","affiliations":[{"raw_affiliation_string":"VeNIT Lab, Marmara University, Turkey","institution_ids":["https://openalex.org/I74897591"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74897591"],"apc_list":null,"apc_paid":null,"fwci":0.7778,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.72958539,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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":0.9997000098228455,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T13918","display_name":"Advanced Data and IoT Technologies","score":0.9944000244140625,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7798296809196472},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.638077974319458},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.6034209728240967},{"id":"https://openalex.org/keywords/edge-computing","display_name":"Edge computing","score":0.5887136459350586},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.5370129346847534},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.5147769451141357},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5106292366981506},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5099009275436401},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.48889389634132385},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.47831347584724426},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.4773597717285156},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.42909902334213257},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.4235362708568573},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39693284034729004},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.3914479613304138},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.12191057205200195},{"id":"https://openalex.org/keywords/software-engineering","display_name":"Software engineering","score":0.09783965349197388}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7798296809196472},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.638077974319458},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.6034209728240967},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.5887136459350586},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.5370129346847534},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.5147769451141357},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5106292366981506},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5099009275436401},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.48889389634132385},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.47831347584724426},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.4773597717285156},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.42909902334213257},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4235362708568573},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39693284034729004},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3914479613304138},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.12191057205200195},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.09783965349197388},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3582177.3582178","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3582177.3582178","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1965484410","https://openalex.org/W2803421549","https://openalex.org/W2884367402","https://openalex.org/W2915771847","https://openalex.org/W2971329741","https://openalex.org/W2997192890","https://openalex.org/W3014364594","https://openalex.org/W3018620108","https://openalex.org/W3034745255","https://openalex.org/W3035829231","https://openalex.org/W3113175648","https://openalex.org/W3122799380","https://openalex.org/W3126631790","https://openalex.org/W3173255485","https://openalex.org/W3175158881","https://openalex.org/W3184439416","https://openalex.org/W3199518238","https://openalex.org/W3208954537","https://openalex.org/W4206272272","https://openalex.org/W4225714398","https://openalex.org/W4297676427","https://openalex.org/W4297818349","https://openalex.org/W4320487943","https://openalex.org/W4386076325","https://openalex.org/W6639102338","https://openalex.org/W6798838024"],"related_works":["https://openalex.org/W4322761281","https://openalex.org/W4238233472","https://openalex.org/W4312996489","https://openalex.org/W3111395152","https://openalex.org/W4313526662","https://openalex.org/W3106131444","https://openalex.org/W3216099748","https://openalex.org/W4205963435","https://openalex.org/W4313463379","https://openalex.org/W3214037210"],"abstract_inverted_index":{"Real-time":[0],"objection":[1],"detection":[2,70],"is":[3,154],"becoming":[4],"more":[5,30],"important":[6],"and":[7,16,39,75,121,136],"critical":[8],"in":[9,146],"all":[10],"application":[11],"areas,":[12],"including":[13],"Smart":[14,17],"Transport":[15],"City.":[18],"From":[19],"safety/security":[20],"to":[21,60,88,173],"resource":[22],"efficiency,":[23],"real-time":[24,144],"image":[25],"processing":[26],"approaches":[27],"are":[28,123],"used":[29],"than":[31],"ever.":[32],"On":[33],"the":[34,103,160,167],"other":[35,174],"hand,":[36],"low-latency":[37],"requirements":[38],"available":[40,128],"resources":[41,58],"present":[42],"challenges.":[43],"Edge":[44],"computing":[45,49],"integrated":[46],"with":[47,93,110,138],"cloud":[48],"minimizes":[50],"communication":[51],"delays":[52],"but":[53],"requires":[54],"efficient":[55],"use":[56],"of":[57,105,150],"due":[59],"its":[61],"limited":[62],"resources.":[63],"For":[64],"example,":[65],"although":[66],"deep":[67,90,107],"learning-based":[68],"object":[69],"methods":[71],"give":[72],"very":[73],"accurate":[74],"reliable":[76],"results,":[77],"they":[78],"require":[79],"high":[80],"computational":[81],"power.":[82],"This":[83],"overhead":[84],"reveals":[85],"a":[86,126],"need":[87],"implement":[89],"learning":[91,108],"models":[92,109],"less":[94],"complex":[95],"architectures":[96],"for":[97],"edge":[98,129],"deployment.":[99],"In":[100],"this":[101],"paper,":[102],"performance":[104],"evolving":[106],"their":[111,139],"lightweight":[112],"versions":[113,141],"such":[114],"as":[115],"YOLOv5-Nano,":[116],"YOLOX-Nano,":[117],"YOLOX-Tiny,":[118],"YOLOv6-Nano,":[119],"YOLOv6-Tiny,":[120],"YOLOv7-Tiny":[122],"evaluated":[124],"on":[125],"commercially":[127],"device.":[130],"The":[131],"results":[132],"show":[133],"that":[134,157],"YOLOv5-Nano":[135,165],"YOLOv6-Nano":[137],"TensorRT":[140],"can":[142],"provide":[143],"applicability":[145],"approximately":[147],"35":[148],"milliseconds":[149],"inference":[151],"time.":[152],"It":[153],"also":[155],"observed":[156],"YOLOv6-Tiny":[158],"gives":[159,166],"highest":[161],"average":[162],"precision":[163],"while":[164],"lowest":[168],"energy":[169],"consumption":[170],"when":[171],"compared":[172],"models.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
