{"id":"https://openalex.org/W3034921093","doi":"https://doi.org/10.1109/icmew46912.2020.9105994","title":"An Embedded Deep Learning Object Detection Model For Traffic In Asian Countries","display_name":"An Embedded Deep Learning Object Detection Model For Traffic In Asian Countries","publication_year":2020,"publication_date":"2020-06-09","ids":{"openalex":"https://openalex.org/W3034921093","doi":"https://doi.org/10.1109/icmew46912.2020.9105994","mag":"3034921093"},"language":"en","primary_location":{"id":"doi:10.1109/icmew46912.2020.9105994","is_oa":true,"landing_page_url":"https://doi.org/10.1109/icmew46912.2020.9105994","pdf_url":"https://ieeexplore.ieee.org/ielx7/9099356/9105943/09105994.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multimedia &amp; Expo Workshops (ICMEW)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/9099356/9105943/09105994.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101533936","display_name":"Wei-Ju Chen","orcid":"https://orcid.org/0000-0001-5978-9235"},"institutions":[{"id":"https://openalex.org/I318025780","display_name":"Foxconn (United States)","ror":"https://ror.org/043xc2b47","country_code":"US","type":"company","lineage":["https://openalex.org/I318025780","https://openalex.org/I4210108919"]},{"id":"https://openalex.org/I4210125478","display_name":"Foxconn (United Kingdom)","ror":"https://ror.org/030xy3q54","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210108919","https://openalex.org/I4210125478"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Weiju Chen","raw_affiliation_strings":["Foxconn (Hon Hai) Technology Group,Industrial Big Data Office","Industrial Big Data Office, Foxconn (Hon Hai) Technology Group"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Foxconn (Hon Hai) Technology Group,Industrial Big Data Office","institution_ids":["https://openalex.org/I318025780","https://openalex.org/I4210125478"]},{"raw_affiliation_string":"Industrial Big Data Office, Foxconn (Hon Hai) Technology Group","institution_ids":["https://openalex.org/I318025780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006028738","display_name":"Wan-Chen Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I318025780","display_name":"Foxconn (United States)","ror":"https://ror.org/043xc2b47","country_code":"US","type":"company","lineage":["https://openalex.org/I318025780","https://openalex.org/I4210108919"]},{"id":"https://openalex.org/I4210125478","display_name":"Foxconn (United Kingdom)","ror":"https://ror.org/030xy3q54","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210108919","https://openalex.org/I4210125478"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"WanChen Wu","raw_affiliation_strings":["Foxconn (Hon Hai) Technology Group,Industrial Big Data Office","Industrial Big Data Office, Foxconn (Hon Hai) Technology Group"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Foxconn (Hon Hai) Technology Group,Industrial Big Data Office","institution_ids":["https://openalex.org/I318025780","https://openalex.org/I4210125478"]},{"raw_affiliation_string":"Industrial Big Data Office, Foxconn (Hon Hai) Technology Group","institution_ids":["https://openalex.org/I318025780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104306287","display_name":"Hao\u2010Wei Chang","orcid":null},"institutions":[{"id":"https://openalex.org/I318025780","display_name":"Foxconn (United States)","ror":"https://ror.org/043xc2b47","country_code":"US","type":"company","lineage":["https://openalex.org/I318025780","https://openalex.org/I4210108919"]},{"id":"https://openalex.org/I4210125478","display_name":"Foxconn (United Kingdom)","ror":"https://ror.org/030xy3q54","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210108919","https://openalex.org/I4210125478"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Hao-Wei Chang","raw_affiliation_strings":["Foxconn (Hon Hai) Technology Group,Industrial Big Data Office","Industrial Big Data Office, Foxconn (Hon Hai) Technology Group"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Foxconn (Hon Hai) Technology Group,Industrial Big Data Office","institution_ids":["https://openalex.org/I318025780","https://openalex.org/I4210125478"]},{"raw_affiliation_string":"Industrial Big Data Office, Foxconn (Hon Hai) Technology Group","institution_ids":["https://openalex.org/I318025780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101063960","display_name":"Wei-Liang Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I318025780","display_name":"Foxconn (United States)","ror":"https://ror.org/043xc2b47","country_code":"US","type":"company","lineage":["https://openalex.org/I318025780","https://openalex.org/I4210108919"]},{"id":"https://openalex.org/I4210125478","display_name":"Foxconn (United Kingdom)","ror":"https://ror.org/030xy3q54","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210108919","https://openalex.org/I4210125478"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Wei-Liang Lin","raw_affiliation_strings":["Foxconn (Hon Hai) Technology Group,Industrial Big Data Office","Industrial Big Data Office, Foxconn (Hon Hai) Technology Group"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Foxconn (Hon Hai) Technology Group,Industrial Big Data Office","institution_ids":["https://openalex.org/I318025780","https://openalex.org/I4210125478"]},{"raw_affiliation_string":"Industrial Big Data Office, Foxconn (Hon Hai) Technology Group","institution_ids":["https://openalex.org/I318025780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074951394","display_name":"Changhua Yang","orcid":"https://orcid.org/0009-0000-4251-7874"},"institutions":[{"id":"https://openalex.org/I318025780","display_name":"Foxconn (United States)","ror":"https://ror.org/043xc2b47","country_code":"US","type":"company","lineage":["https://openalex.org/I318025780","https://openalex.org/I4210108919"]},{"id":"https://openalex.org/I4210125478","display_name":"Foxconn (United Kingdom)","ror":"https://ror.org/030xy3q54","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210108919","https://openalex.org/I4210125478"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Changhua Yang","raw_affiliation_strings":["Foxconn (Hon Hai) Technology Group,Industrial Big Data Office","Industrial Big Data Office, Foxconn (Hon Hai) Technology Group"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Foxconn (Hon Hai) Technology Group,Industrial Big Data Office","institution_ids":["https://openalex.org/I318025780","https://openalex.org/I4210125478"]},{"raw_affiliation_string":"Industrial Big Data Office, Foxconn (Hon Hai) Technology Group","institution_ids":["https://openalex.org/I318025780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088772100","display_name":"Yangwei Liu","orcid":"https://orcid.org/0000-0002-9465-1220"},"institutions":[{"id":"https://openalex.org/I318025780","display_name":"Foxconn (United States)","ror":"https://ror.org/043xc2b47","country_code":"US","type":"company","lineage":["https://openalex.org/I318025780","https://openalex.org/I4210108919"]},{"id":"https://openalex.org/I4210125478","display_name":"Foxconn (United Kingdom)","ror":"https://ror.org/030xy3q54","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210108919","https://openalex.org/I4210125478"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Yang-wei Liu","raw_affiliation_strings":["Foxconn (Hon Hai) Technology Group,Industrial Big Data Office","Industrial Big Data Office, Foxconn (Hon Hai) Technology Group"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Foxconn (Hon Hai) Technology Group,Industrial Big Data Office","institution_ids":["https://openalex.org/I318025780","https://openalex.org/I4210125478"]},{"raw_affiliation_string":"Industrial Big Data Office, Foxconn (Hon Hai) Technology Group","institution_ids":["https://openalex.org/I318025780"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0979,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.38557008,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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":0.9846000075340271,"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":0.9843999743461609,"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/deep-learning","display_name":"Deep learning","score":0.806638240814209},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7887542247772217},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7836889028549194},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7823938727378845},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6796034574508667},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4896426796913147},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4824962615966797},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4758740961551666},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4477522075176239},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4469706118106842},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.44645795226097107},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.426891028881073},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40920352935791016},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3407450020313263},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.17202439904212952},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07261577248573303},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.07175078988075256}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.806638240814209},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7887542247772217},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7836889028549194},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7823938727378845},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6796034574508667},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4896426796913147},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4824962615966797},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4758740961551666},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4477522075176239},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4469706118106842},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44645795226097107},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.426891028881073},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40920352935791016},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3407450020313263},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.17202439904212952},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07261577248573303},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.07175078988075256},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmew46912.2020.9105994","is_oa":true,"landing_page_url":"https://doi.org/10.1109/icmew46912.2020.9105994","pdf_url":"https://ieeexplore.ieee.org/ielx7/9099356/9105943/09105994.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multimedia &amp; Expo Workshops (ICMEW)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1109/icmew46912.2020.9105994","is_oa":true,"landing_page_url":"https://doi.org/10.1109/icmew46912.2020.9105994","pdf_url":"https://ieeexplore.ieee.org/ielx7/9099356/9105943/09105994.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multimedia &amp; Expo Workshops (ICMEW)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.41999998688697815,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3034921093.pdf","grobid_xml":"https://content.openalex.org/works/W3034921093.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2102605133","https://openalex.org/W2109255472","https://openalex.org/W2163605009","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2565639579","https://openalex.org/W2962766617","https://openalex.org/W2963351448","https://openalex.org/W2964241181","https://openalex.org/W2969937244","https://openalex.org/W3009131247","https://openalex.org/W3042011474","https://openalex.org/W3106250896","https://openalex.org/W4293584584","https://openalex.org/W6620707391","https://openalex.org/W6684191040","https://openalex.org/W6750227808","https://openalex.org/W6766837696","https://openalex.org/W6771154167","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W2949096641","https://openalex.org/W2970686063","https://openalex.org/W2969228573","https://openalex.org/W4320729701"],"abstract_inverted_index":{"Deep":[0],"learning-based":[1],"object":[2,33,113],"detection":[3,34,114],"algorithms":[4,35],"have":[5,75],"been":[6,100],"developed":[7],"through":[8],"extensive":[9],"research":[10],"and":[11,65,93],"achieved":[12],"impressive":[13],"performance.":[14],"However,":[15],"due":[16],"to":[17,30,83],"the":[18,70,77,85,103,108],"heavy":[19],"computation":[20],"of":[21,87],"deep":[22,111],"convolutional":[23,62],"neural":[24,63],"networks,":[25],"it":[26],"is":[27,53],"not":[28],"suitable":[29],"deploy":[31],"all":[32],"on":[36,79],"resource-constrained":[37],"embedded":[38,110],"systems,":[39],"such":[40],"as":[41,102],"NVIDIA":[42,80],"Jetson":[43,81],"TX2.":[44],"In":[45],"this":[46,97],"paper,":[47],"a":[48,59,66],"size-efficient":[49],"YOLO":[50],"v3-based":[51],"model":[52,78,89,115],"adopted":[54],"with":[55],"two":[56],"key":[57],"points,":[58],"better":[60],"backbone":[61],"network":[64],"fusing":[67],"structure":[68],"for":[69,118],"multiscale":[71],"feature":[72],"map.":[73],"We":[74],"deployed":[76],"TX2":[82],"test":[84],"performance":[86],"accuracy,":[88],"size,":[90],"computational":[91],"complexity":[92],"detecting":[94],"time.":[95],"Furthermore,":[96],"methodology":[98],"has":[99],"awarded":[101],"best":[104],"bicycle":[105],"detector":[106],"in":[107,120],"2020":[109],"learning":[112],"compression":[116],"competition":[117],"traffic":[119],"Asian":[121],"countries.":[122]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
