{"id":"https://openalex.org/W2620908499","doi":"https://doi.org/10.1177/0037549717709932","title":"Object recognition and detection with deep learning for autonomous driving applications","display_name":"Object recognition and detection with deep learning for autonomous driving applications","publication_year":2017,"publication_date":"2017-06-02","ids":{"openalex":"https://openalex.org/W2620908499","doi":"https://doi.org/10.1177/0037549717709932","mag":"2620908499"},"language":"en","primary_location":{"id":"doi:10.1177/0037549717709932","is_oa":false,"landing_page_url":"https://doi.org/10.1177/0037549717709932","pdf_url":null,"source":{"id":"https://openalex.org/S32573412","display_name":"SIMULATION","issn_l":"0037-5497","issn":["0037-5497","1741-3133"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIMULATION","raw_type":"journal-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/A5049762746","display_name":"Ay\u015feg\u00fcl U\u00e7ar","orcid":"https://orcid.org/0000-0002-5253-3779"},"institutions":[{"id":"https://openalex.org/I143396566","display_name":"F\u0131rat University","ror":"https://ror.org/05teb7b63","country_code":"TR","type":"education","lineage":["https://openalex.org/I143396566"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Ay\u015feg\u00fcl U\u00e7ar","raw_affiliation_strings":["F\u0131rat University, Department of Mechatronic Engineering, Elaz\u0131\u011f, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"F\u0131rat University, Department of Mechatronic Engineering, Elaz\u0131\u011f, Turkey","institution_ids":["https://openalex.org/I143396566"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102706549","display_name":"Yakup Demir","orcid":"https://orcid.org/0000-0001-9530-5824"},"institutions":[{"id":"https://openalex.org/I143396566","display_name":"F\u0131rat University","ror":"https://ror.org/05teb7b63","country_code":"TR","type":"education","lineage":["https://openalex.org/I143396566"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Yakup Demir","raw_affiliation_strings":["F\u0131rat University, Department of Electrical Electronics Engineering, Elaz\u0131\u011f, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"F\u0131rat University, Department of Electrical Electronics Engineering, Elaz\u0131\u011f, Turkey","institution_ids":["https://openalex.org/I143396566"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063349801","display_name":"C\u00fcneyt G\u00fczel\u0456\u0219","orcid":"https://orcid.org/0000-0001-5416-368X"},"institutions":[{"id":"https://openalex.org/I59944920","display_name":"Ya\u015far University","ror":"https://ror.org/00dz1eb96","country_code":"TR","type":"education","lineage":["https://openalex.org/I59944920"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"C\u00fcneyt G\u00fczeli\u015f","raw_affiliation_strings":["Ya\u015far University, Department of Electrical Electronics Engineering, \u0130zmir, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ya\u015far University, Department of Electrical Electronics Engineering, \u0130zmir, Turkey","institution_ids":["https://openalex.org/I59944920"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5049762746"],"corresponding_institution_ids":["https://openalex.org/I143396566"],"apc_list":null,"apc_paid":null,"fwci":3.9739,"has_fulltext":false,"cited_by_count":183,"citation_normalized_percentile":{"value":0.96321477,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"93","issue":"9","first_page":"759","last_page":"769"},"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":0.9995999932289124,"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":0.9965999722480774,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8140295743942261},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7952843308448792},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6988527178764343},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6783217191696167},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.664068341255188},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6568459272384644},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.6518633961677551},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6081766486167908},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5950407981872559},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5374053120613098},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39081114530563354},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.1607980728149414},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07671922445297241}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8140295743942261},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7952843308448792},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6988527178764343},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6783217191696167},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.664068341255188},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6568459272384644},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.6518633961677551},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6081766486167908},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5950407981872559},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5374053120613098},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39081114530563354},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.1607980728149414},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07671922445297241},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1177/0037549717709932","is_oa":false,"landing_page_url":"https://doi.org/10.1177/0037549717709932","pdf_url":null,"source":{"id":"https://openalex.org/S32573412","display_name":"SIMULATION","issn_l":"0037-5497","issn":["0037-5497","1741-3133"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIMULATION","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W819977924","https://openalex.org/W1202196558","https://openalex.org/W1484748859","https://openalex.org/W1491719799","https://openalex.org/W1650122911","https://openalex.org/W1677409904","https://openalex.org/W1963882359","https://openalex.org/W1994002998","https://openalex.org/W2004294122","https://openalex.org/W2025401819","https://openalex.org/W2031454541","https://openalex.org/W2067502213","https://openalex.org/W2084567445","https://openalex.org/W2084997728","https://openalex.org/W2097018403","https://openalex.org/W2101926813","https://openalex.org/W2104371740","https://openalex.org/W2109255472","https://openalex.org/W2111072639","https://openalex.org/W2111574971","https://openalex.org/W2113242816","https://openalex.org/W2115733720","https://openalex.org/W2117539524","https://openalex.org/W2117731089","https://openalex.org/W2119821739","https://openalex.org/W2123099218","https://openalex.org/W2124386111","https://openalex.org/W2130306094","https://openalex.org/W2133755669","https://openalex.org/W2134380836","https://openalex.org/W2137664016","https://openalex.org/W2141584146","https://openalex.org/W2145072179","https://openalex.org/W2147800946","https://openalex.org/W2150341604","https://openalex.org/W2153008989","https://openalex.org/W2156547346","https://openalex.org/W2161969291","https://openalex.org/W2162741153","https://openalex.org/W2164598857","https://openalex.org/W2166049352","https://openalex.org/W2168091201","https://openalex.org/W2200528286","https://openalex.org/W2265127172","https://openalex.org/W2409805034","https://openalex.org/W2522025381","https://openalex.org/W2524324335","https://openalex.org/W2536219127","https://openalex.org/W2554078916","https://openalex.org/W2560835477","https://openalex.org/W2618530766","https://openalex.org/W2949493420","https://openalex.org/W2950561226","https://openalex.org/W2953106684","https://openalex.org/W3023742835","https://openalex.org/W4229799326","https://openalex.org/W4239510810","https://openalex.org/W6725168888"],"related_works":["https://openalex.org/W1487808658","https://openalex.org/W2802018156","https://openalex.org/W2101531944","https://openalex.org/W4313315626","https://openalex.org/W2913302899","https://openalex.org/W2922437833","https://openalex.org/W4223892596","https://openalex.org/W4312696271","https://openalex.org/W2933098581","https://openalex.org/W2556125083"],"abstract_inverted_index":{"Autonomous":[0],"driving":[1],"requires":[2],"reliable":[3],"and":[4,7,31,55,67,85,113,144,154,161],"accurate":[5],"detection":[6,19,156],"recognition":[8,153],"of":[9,118,128],"surrounding":[10],"objects":[11],"in":[12],"real":[13],"drivable":[14],"environments.":[15],"Although":[16],"different":[17],"object":[18,92,152],"algorithms":[20],"have":[21],"been":[22],"proposed,":[23],"not":[24],"all":[25],"are":[26],"robust":[27,68],"enough":[28],"to":[29,61,89,123,166],"detect":[30],"recognize":[32],"occluded":[33],"or":[34],"truncated":[35],"objects.":[36],"In":[37,72,137],"this":[38],"paper,":[39],"we":[40,76,95,133,139],"propose":[41],"a":[42,120,145],"novel":[43],"hybrid":[44],"Local":[45],"Multiple":[46],"system":[47,175],"(LM-CNN-SVM)":[48],"based":[49],"on":[50,158],"Convolutional":[51],"Neural":[52],"Networks":[53],"(CNNs)":[54],"Support":[56],"Vector":[57],"Machines":[58],"(SVMs)":[59],"due":[60],"their":[62],"powerful":[63],"feature":[64],"extraction":[65],"capability":[66],"classification":[69],"property,":[70],"respectively.":[71],"the":[73,79,125,129,141,159,167,173],"proposed":[74,174],"system,":[75],"divide":[77],"first":[78],"whole":[80],"image":[81],"into":[82,107],"local":[83,91],"regions":[84],"employ":[86],"multiple":[87,108],"CNNs":[88],"learn":[90],"features.":[93],"Secondly,":[94],"select":[96],"discriminative":[97],"features":[98],"by":[99],"using":[100,119],"Principal":[101],"Component":[102],"Analysis.":[103],"We":[104,149],"then":[105],"import":[106],"SVMs":[109],"applying":[110],"both":[111],"empirical":[112],"structural":[114],"risk":[115],"minimization":[116],"instead":[117],"direct":[121],"CNN":[122,147],"increase":[124],"generalization":[126],"ability":[127],"classifier":[130],"system.":[131],"Finally,":[132],"fuse":[134],"SVM":[135],"outputs.":[136],"addition,":[138],"use":[140],"pre-trained":[142],"AlexNet":[143],"new":[146],"architecture.":[148],"carry":[150],"out":[151],"pedestrian":[155],"experiments":[157],"Caltech-101":[160],"Caltech":[162],"Pedestrian":[163],"datasets.":[164],"Comparisons":[165],"best":[168],"state-of-the-art":[169],"methods":[170],"show":[171],"that":[172],"achieved":[176],"better":[177],"results.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":21},{"year":2024,"cited_by_count":29},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":33},{"year":2021,"cited_by_count":36},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":11},{"year":2018,"cited_by_count":16},{"year":2017,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}