{"id":"https://openalex.org/W2962982769","doi":"https://doi.org/10.1109/ivs.2017.7995812","title":"Scale optimization for full-image-CNN vehicle detection","display_name":"Scale optimization for full-image-CNN vehicle detection","publication_year":2017,"publication_date":"2017-06-01","ids":{"openalex":"https://openalex.org/W2962982769","doi":"https://doi.org/10.1109/ivs.2017.7995812","mag":"2962982769"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2017.7995812","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2017.7995812","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5074250521","display_name":"Yang Gao","orcid":"https://orcid.org/0000-0002-2488-1813"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Yang Gao","raw_affiliation_strings":["Joint Institute of Engineering, Sun Yat-sen University - Carnegie Mellon University, 510006 Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Joint Institute of Engineering, Sun Yat-sen University - Carnegie Mellon University, 510006 Guangdong, China","institution_ids":["https://openalex.org/I157773358","https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068598044","display_name":"Shouyan Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Shouyan Guo","raw_affiliation_strings":["Joint Institute of Engineering, Sun Yat-sen University - Carnegie Mellon University, 510006 Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Joint Institute of Engineering, Sun Yat-sen University - Carnegie Mellon University, 510006 Guangdong, China","institution_ids":["https://openalex.org/I157773358","https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049617330","display_name":"Kaimin Huang","orcid":"https://orcid.org/0000-0001-9785-3736"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Kaimin Huang","raw_affiliation_strings":["Joint Institute of Engineering, Sun Yat-sen University - Carnegie Mellon University, 510006 Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Joint Institute of Engineering, Sun Yat-sen University - Carnegie Mellon University, 510006 Guangdong, China","institution_ids":["https://openalex.org/I157773358","https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100360540","display_name":"Jiaxin Chen","orcid":"https://orcid.org/0000-0003-1506-2223"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Jiaxin Chen","raw_affiliation_strings":["Joint Institute of Engineering, Sun Yat-sen University - Carnegie Mellon University, 510006 Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Joint Institute of Engineering, Sun Yat-sen University - Carnegie Mellon University, 510006 Guangdong, China","institution_ids":["https://openalex.org/I157773358","https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063502813","display_name":"Qian Gong","orcid":"https://orcid.org/0000-0002-3570-4142"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Qian Gong","raw_affiliation_strings":["Joint Institute of Engineering, Sun Yat-sen University - Carnegie Mellon University, 510006 Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Joint Institute of Engineering, Sun Yat-sen University - Carnegie Mellon University, 510006 Guangdong, China","institution_ids":["https://openalex.org/I157773358","https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102871171","display_name":"Yang Zou","orcid":"https://orcid.org/0000-0003-0396-7850"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Yang Zou","raw_affiliation_strings":["Joint Institute of Engineering, Sun Yat-sen University - Carnegie Mellon University, 510006 Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Joint Institute of Engineering, Sun Yat-sen University - Carnegie Mellon University, 510006 Guangdong, China","institution_ids":["https://openalex.org/I157773358","https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056006887","display_name":"Tong Bai","orcid":"https://orcid.org/0000-0001-5612-0675"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Tong Bai","raw_affiliation_strings":["Joint Institute of Engineering, Sun Yat-sen University - Carnegie Mellon University, 510006 Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Joint Institute of Engineering, Sun Yat-sen University - Carnegie Mellon University, 510006 Guangdong, China","institution_ids":["https://openalex.org/I157773358","https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026054182","display_name":"Gary Overett","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Gary Overett","raw_affiliation_strings":["Joint Institute of Engineering, Sun Yat-sen University - Carnegie Mellon University, 510006 Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Joint Institute of Engineering, Sun Yat-sen University - Carnegie Mellon University, 510006 Guangdong, China","institution_ids":["https://openalex.org/I157773358","https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7955,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.8593229,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"785","last_page":"791"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9973999857902527,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.991100013256073,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8528265953063965},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8027138710021973},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7369779348373413},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6740173697471619},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6700400710105896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6082984805107117},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6074114441871643},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5787876844406128},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5080525875091553},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48342645168304443},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4775034785270691},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.4557878077030182},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.36394333839416504},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3491423428058624},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.321346253156662}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8528265953063965},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8027138710021973},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7369779348373413},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6740173697471619},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6700400710105896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6082984805107117},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6074114441871643},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5787876844406128},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5080525875091553},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48342645168304443},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4775034785270691},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.4557878077030182},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.36394333839416504},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3491423428058624},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.321346253156662},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/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/ivs.2017.7995812","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2017.7995812","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7699999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W7746136","https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1849277567","https://openalex.org/W1861492603","https://openalex.org/W1934410531","https://openalex.org/W1998808035","https://openalex.org/W2031489346","https://openalex.org/W2088049833","https://openalex.org/W2097117768","https://openalex.org/W2102605133","https://openalex.org/W2115579991","https://openalex.org/W2117539524","https://openalex.org/W2125186487","https://openalex.org/W2155893237","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2164598857","https://openalex.org/W2186094539","https://openalex.org/W2194775991","https://openalex.org/W2247526854","https://openalex.org/W2497039038","https://openalex.org/W2508384486","https://openalex.org/W2613718673","https://openalex.org/W2963037989","https://openalex.org/W6600313631","https://openalex.org/W6620707391","https://openalex.org/W6639102338","https://openalex.org/W6639204139","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2110523656","https://openalex.org/W1482209366","https://openalex.org/W2521627374","https://openalex.org/W2981954115"],"abstract_inverted_index":{"Many":[0],"state-of-the-art":[1],"general":[2],"object":[3,69,87,99],"detection":[4,88,119,175],"methods":[5],"make":[6],"use":[7,80],"of":[8,57,78,83,108,129,161],"shared":[9],"full-image":[10,137],"convolutional":[11,163],"features":[12,139],"(as":[13],"in":[14,148,193],"Faster":[15,85,188],"R-CNN).":[16],"This":[17],"achieves":[18],"a":[19,62,65],"reasonable":[20],"test-phase":[21],"computation":[22],"time":[23],"while":[24],"enjoys":[25],"the":[26,58,79,84,109,114,127,141,149,162,178],"discriminative":[27],"power":[28],"provided":[29],"by":[30,124,154],"large":[31],"Convolutional":[32],"Neural":[33],"Network":[34],"(CNN)":[35],"models.":[36],"Such":[37],"designs":[38],"excel":[39],"on":[40,169,185],"benchmarks1which":[41],"contain":[42],"natural":[43,93],"images":[44],"but":[45],"which":[46],"have":[47,53],"very":[48],"unnatural":[49],"distributions,":[50],"i.e.":[51],"they":[52],"an":[54],"unnaturally":[55],"high-frequency":[56],"target":[59],"classes":[60],"and":[61,81,96,132,153],"bias":[63],"towards":[64],"\u201cfriendly\u201d":[66],"or":[67],"\u201cdominant\u201d":[68],"scale.":[70],"In":[71,101],"this":[72,123],"paper":[73],"we":[74,103,166],"present":[75],"further":[76],"study":[77],"adaptation":[82],"R-CNN":[86,189],"method":[89],"for":[90,177],"datasets":[91],"presenting":[92],"scale":[94,111],"distribution":[95,116],"unbiased":[97],"real-world":[98],"frequency.":[100],"particular,":[102],"show":[104],"that":[105],"better":[106,146],"alignment":[107],"detector":[110,190],"sensitivity":[112],"to":[113,191],"extant":[115],"improves":[117],"vehicle":[118],"performance.":[120],"We":[121,172],"do":[122],"modifying":[125],"both":[126],"selection":[128],"Region":[130],"Proposals,":[131],"through":[133,158],"using":[134],"more":[135],"scale-appropriate":[136],"convolution":[138],"within":[140],"CNN":[142],"model.":[143],"By":[144],"selecting":[145],"scales":[147],"region":[150],"proposal":[151],"input":[152],"combining":[155],"feature":[156],"maps":[157],"careful":[159],"design":[160],"neural":[164],"network,":[165],"improve":[167],"performance":[168],"smaller":[170],"objects.":[171],"significantly":[173],"increase":[174],"AP":[176],"KITTI":[179],"dataset":[180],"car":[181],"class":[182],"from":[183],"76.3%":[184],"our":[186,194],"baseline":[187],"83.6%":[192],"improved":[195],"detector.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1}],"updated_date":"2026-08-08T01:25:22.217667","created_date":"2025-10-10T00:00:00"}
