{"id":"https://openalex.org/W4205564480","doi":"https://doi.org/10.1109/m2vip49856.2021.9665042","title":"Application Optimization of Fine-grained Vehicle Classification based on Backbone Network","display_name":"Application Optimization of Fine-grained Vehicle Classification based on Backbone Network","publication_year":2021,"publication_date":"2021-11-26","ids":{"openalex":"https://openalex.org/W4205564480","doi":"https://doi.org/10.1109/m2vip49856.2021.9665042"},"language":"en","primary_location":{"id":"doi:10.1109/m2vip49856.2021.9665042","is_oa":false,"landing_page_url":"https://doi.org/10.1109/m2vip49856.2021.9665042","pdf_url":null,"source":{"id":"https://openalex.org/S4363608277","display_name":"2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","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/A5053705822","display_name":"Xiaoshun Xu","orcid":"https://orcid.org/0000-0002-7553-5558"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]},{"id":"https://openalex.org/I4210091156","display_name":"SAIC-GM (China)","ror":"https://ror.org/00h64t852","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091156"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoshun Xu","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China","University and SAIC General Motors Co., Ltd, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"University and SAIC General Motors Co., Ltd, Shanghai, China","institution_ids":["https://openalex.org/I4210091156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109057441","display_name":"Jinqiu Mo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210091156","display_name":"SAIC-GM (China)","ror":"https://ror.org/00h64t852","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091156"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinqiu Mo","raw_affiliation_strings":["University and SAIC General Motors Co., Ltd, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University and SAIC General Motors Co., Ltd, Shanghai, China","institution_ids":["https://openalex.org/I4210091156"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100337178","display_name":"Min Chen","orcid":"https://orcid.org/0000-0001-5320-5729"},"institutions":[{"id":"https://openalex.org/I4210091156","display_name":"SAIC-GM (China)","ror":"https://ror.org/00h64t852","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091156"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Chen","raw_affiliation_strings":["University and SAIC General Motors Co., Ltd, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University and SAIC General Motors Co., Ltd, Shanghai, China","institution_ids":["https://openalex.org/I4210091156"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0656,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.36848138,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"54","last_page":"59"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9991000294685364,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9955999851226807,"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/computer-science","display_name":"Computer science","score":0.801541805267334},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7382768988609314},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5852201581001282},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5517914295196533},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.505954921245575},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49776318669319153},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47821590304374695},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47793108224868774},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47632986307144165},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41470232605934143}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.801541805267334},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7382768988609314},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5852201581001282},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5517914295196533},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.505954921245575},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49776318669319153},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47821590304374695},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47793108224868774},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47632986307144165},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41470232605934143},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/m2vip49856.2021.9665042","is_oa":false,"landing_page_url":"https://doi.org/10.1109/m2vip49856.2021.9665042","pdf_url":null,"source":{"id":"https://openalex.org/S4363608277","display_name":"2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W56385144","https://openalex.org/W2091759811","https://openalex.org/W2097117768","https://openalex.org/W2104657103","https://openalex.org/W2133665775","https://openalex.org/W2138011018","https://openalex.org/W2183341477","https://openalex.org/W2579318141","https://openalex.org/W2620694480","https://openalex.org/W2737725206","https://openalex.org/W2763070548","https://openalex.org/W2773003563","https://openalex.org/W2783365216","https://openalex.org/W2883502031","https://openalex.org/W2948210185","https://openalex.org/W2955425717","https://openalex.org/W2963163009","https://openalex.org/W2963420686","https://openalex.org/W2964189431","https://openalex.org/W2964350391","https://openalex.org/W2998619563","https://openalex.org/W3036351887","https://openalex.org/W3037793940","https://openalex.org/W3102622140","https://openalex.org/W3108870912","https://openalex.org/W3139434170","https://openalex.org/W3145444543","https://openalex.org/W3205974013","https://openalex.org/W4288622677","https://openalex.org/W6676165494","https://openalex.org/W6743731764","https://openalex.org/W6759000249","https://openalex.org/W6762718338","https://openalex.org/W6779752386","https://openalex.org/W6792309431","https://openalex.org/W6793164127"],"related_works":["https://openalex.org/W2055243143","https://openalex.org/W4321636575","https://openalex.org/W1986418932","https://openalex.org/W2357796999","https://openalex.org/W2045526782","https://openalex.org/W3147584709","https://openalex.org/W2741131631","https://openalex.org/W2156919374","https://openalex.org/W1984019423","https://openalex.org/W2961085424"],"abstract_inverted_index":{"As":[0],"an":[1],"important":[2],"branch":[3],"of":[4,16,22,35,85,124,150],"fine-grained":[5,11,39],"visual":[6],"classification,":[7],"relevant":[8],"research":[9],"on":[10,44,82,115],"vehicle":[12,40,87],"model":[13,41,88],"classification":[14,42,133],"is":[15,63,101],"great":[17],"significance":[18],"to":[19,103,136],"the":[20,45,60,83,86,109,120,137,144,151],"promotion":[21],"smart":[23],"travel":[24],"and":[25,69,111,122,146],"transportation":[26],"applications.":[27],"In":[28,79],"this":[29,156],"paper,":[30],"we":[31],"propose":[32],"a":[33,90,131],"set":[34],"optimization":[36],"schemes":[37],"for":[38,143],"based":[43,81],"backbone":[46],"network":[47],"like":[48],"EfficientNet.":[49],"The":[50],"presented":[51],"schemes,":[52],"which":[53,128],"have":[54],"been":[55],"relatively":[56],"few":[57],"studied":[58],"in":[59,155],"other":[61],"networks,":[62],"optimized":[64],"through":[65],"input":[66],"data,":[67],"training":[68,110],"inference":[70,112],"process":[71],"with":[72,97],"little":[73],"extra":[74],"costs":[75],"but":[76],"distinct":[77],"benefit.":[78],"particular,":[80],"characteristics":[84],"images,":[89],"novel":[91],"data":[92],"augmentation":[93],"method,":[94,127],"Center":[95],"Fill":[96],"Shifted":[98],"Quadrants":[99],"(CFSQ),":[100],"proposed":[102,126],"facilitate":[104],"effective":[105],"feature":[106],"extraction":[107],"during":[108],"stage.":[113],"Experiments":[114],"Stanford":[116,152],"Car-196":[117,153],"dataset":[118,154],"validate":[119],"accuracy":[121,134],"efficiency":[123],"our":[125],"could":[129],"achieve":[130],"high":[132],"close":[135],"state-of-the-art":[138],"methods.":[139],"Moreover,":[140],"reasonable":[141],"suggestions":[142],"improvement":[145],"application":[147],"are":[148],"provided":[149],"paper.":[157]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}