{"id":"https://openalex.org/W4390485890","doi":"https://doi.org/10.1109/iccma59762.2023.10375062","title":"Multi-Target Classification Using Deep Learning Models for Automotive Applications","display_name":"Multi-Target Classification Using Deep Learning Models for Automotive Applications","publication_year":2023,"publication_date":"2023-11-01","ids":{"openalex":"https://openalex.org/W4390485890","doi":"https://doi.org/10.1109/iccma59762.2023.10375062"},"language":"en","primary_location":{"id":"doi:10.1109/iccma59762.2023.10375062","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iccma59762.2023.10375062","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 11th International Conference on Control, Mechatronics and Automation (ICCMA)","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/A5022050176","display_name":"A Soumya","orcid":null},"institutions":[{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Soumya. A","raw_affiliation_strings":["Indian Institute of Technology,Department of Computer Science and Engineering,Hyderabad,India","Department of Computer Science and Engineering, Indian Institute of Technology, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology,Department of Computer Science and Engineering,Hyderabad,India","institution_ids":["https://openalex.org/I65181880"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, Indian Institute of Technology, Hyderabad, India","institution_ids":["https://openalex.org/I65181880"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014288820","display_name":"Linga Reddy Cenkeramaddi","orcid":"https://orcid.org/0000-0002-1023-2118"},"institutions":[{"id":"https://openalex.org/I200650556","display_name":"University of Agder","ror":"https://ror.org/03x297z98","country_code":"NO","type":"education","lineage":["https://openalex.org/I200650556"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Linga Reddy Cenkeramaddi","raw_affiliation_strings":["University of Agder,Department of Information and Communication Technology,Grimstad,Norway","Department of Information and Communication Technology, University of Agder, Grimstad, Norway"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Agder,Department of Information and Communication Technology,Grimstad,Norway","institution_ids":["https://openalex.org/I200650556"]},{"raw_affiliation_string":"Department of Information and Communication Technology, University of Agder, Grimstad, Norway","institution_ids":["https://openalex.org/I200650556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044985749","display_name":"Chalavadi Vishnu","orcid":"https://orcid.org/0000-0001-9184-3545"},"institutions":[{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Chalavadi Vishnu","raw_affiliation_strings":["Indian Institute of Technology,Department of Computer Science and Engineering,Hyderabad,India","Department of Computer Science and Engineering, Indian Institute of Technology, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology,Department of Computer Science and Engineering,Hyderabad,India","institution_ids":["https://openalex.org/I65181880"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, Indian Institute of Technology, Hyderabad, India","institution_ids":["https://openalex.org/I65181880"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093632214","display_name":"Yash Vinod Lanjewar","orcid":null},"institutions":[{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Yash Vinod Lanjewar","raw_affiliation_strings":["Indian Institute of Technology,Department of Computer Science and Engineering,Hyderabad,India","Department of Computer Science and Engineering, Indian Institute of Technology, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology,Department of Computer Science and Engineering,Hyderabad,India","institution_ids":["https://openalex.org/I65181880"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, Indian Institute of Technology, Hyderabad, India","institution_ids":["https://openalex.org/I65181880"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012566120","display_name":"C. Krishna Mohan","orcid":"https://orcid.org/0000-0002-7316-0836"},"institutions":[{"id":"https://openalex.org/I145894827","display_name":"Indian Institute of Technology Kharagpur","ror":"https://ror.org/03w5sq511","country_code":"IN","type":"education","lineage":["https://openalex.org/I145894827"]},{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Krishna Mohan C","raw_affiliation_strings":["Indian Institute of Technology,Department of Computer Science and Engineering,Hyderabad,India","Department of Computer Science and Engineering, Indian Institute of Technology, Hyderabad, India","Department of Architecture and Regional Planning, Indian Institute of Technology, Kharagpur, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology,Department of Computer Science and Engineering,Hyderabad,India","institution_ids":["https://openalex.org/I65181880"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, Indian Institute of Technology, Hyderabad, India","institution_ids":["https://openalex.org/I65181880"]},{"raw_affiliation_string":"Department of Architecture and Regional Planning, Indian Institute of Technology, Kharagpur, India","institution_ids":["https://openalex.org/I145894827"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17423028,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"31","last_page":"36"},"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":0.9986000061035156,"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":0.9959999918937683,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8079108595848083},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7930517792701721},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7411358952522278},{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.7217123508453369},{"id":"https://openalex.org/keywords/truck","display_name":"Truck","score":0.6607459783554077},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.658986508846283},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6003938317298889},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5971720814704895},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5716629028320312},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5566594004631042},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.533580482006073},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5228019952774048},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.50030517578125},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.483536958694458},{"id":"https://openalex.org/keywords/backbone-network","display_name":"Backbone network","score":0.47350388765335083},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.4733560085296631},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44024670124053955},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3782247006893158},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3673873245716095},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3431508541107178},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10423934459686279}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8079108595848083},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7930517792701721},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7411358952522278},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.7217123508453369},{"id":"https://openalex.org/C52121051","wikidata":"https://www.wikidata.org/wiki/Q43193","display_name":"Truck","level":2,"score":0.6607459783554077},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.658986508846283},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6003938317298889},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5971720814704895},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5716629028320312},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5566594004631042},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.533580482006073},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5228019952774048},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.50030517578125},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.483536958694458},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.47350388765335083},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.4733560085296631},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44024670124053955},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3782247006893158},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3673873245716095},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3431508541107178},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10423934459686279},{"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},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccma59762.2023.10375062","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iccma59762.2023.10375062","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 11th International Conference on Control, Mechatronics and Automation (ICCMA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2108598243","https://openalex.org/W2124244761","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2750936674","https://openalex.org/W2784272016","https://openalex.org/W2793941577","https://openalex.org/W2889079306","https://openalex.org/W2889102127","https://openalex.org/W2891201238","https://openalex.org/W2963163009","https://openalex.org/W2963446712","https://openalex.org/W2971492022","https://openalex.org/W3010398094","https://openalex.org/W3046220160","https://openalex.org/W3108776516","https://openalex.org/W3187838195","https://openalex.org/W4312538217","https://openalex.org/W4367153869","https://openalex.org/W6637373629","https://openalex.org/W6793164127"],"related_works":["https://openalex.org/W1517019597","https://openalex.org/W1968776045","https://openalex.org/W2296713838","https://openalex.org/W767149399","https://openalex.org/W4387272257","https://openalex.org/W2894651257","https://openalex.org/W3200590620","https://openalex.org/W4200172193","https://openalex.org/W4303926741","https://openalex.org/W4200301313"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3],"multi-perspective":[4],"convolutional":[5],"neural":[6],"network":[7,29],"(CNN)":[8],"that":[9,35],"extracts":[10],"the":[11,23,50,106,129,132],"class":[12],"of":[13,93,97,131],"objects":[14,66],"supporting":[15],"intelligent":[16],"transportation":[17],"systems.":[18],"The":[19,40,55,90,125],"proposed":[20,133],"model":[21,41,56,139],"is":[22,57],"visual":[24],"geometry":[25],"group":[26],"(VGG)":[27],"backbone":[28,135],"with":[30,59,82],"custom":[31],"feature":[32],"extraction":[33],"blocks,":[34],"use":[36],"multilayer":[37],"prediction":[38,61],"heads.":[39],"addresses":[42],"both":[43],"multi-class":[44],"and":[45,70,85,103,115,119],"multi-object":[46],"classification":[47,121],"tasks":[48],"utilizing":[49],"automotive":[51,78],"object":[52,72,79],"detection":[53,80],"dataset.":[54],"designed":[58],"multiple":[60],"heads":[62],"to":[63,142],"classify":[64],"different":[65],"in":[67,140],"an":[68],"image":[69],"enable":[71],"count":[73],"prediction.":[74],"A":[75],"publicly":[76],"available":[77],"dataset":[81,91],"19800":[83],"images":[84],"labels":[86],"has":[87],"been":[88],"utilized.":[89],"consists":[92],"five":[94],"primary":[95],"types":[96],"objects:":[98],"Persons,":[99],"Trucks,":[100],"Motorbikes,":[101],"Cars,":[102],"Cyclists.":[104],"On":[105],"dataset,":[107],"pre-trained":[108,144],"models":[109],"such":[110],"as":[111],"VGG,":[112],"Resenet,":[113],"EfficientNet,":[114],"DenseNet":[116],"were":[117],"tested":[118],"their":[120],"performance":[122],"was":[123],"evaluated.":[124],"experimental":[126],"results":[127],"illustrate":[128],"superiority":[130],"VGG":[134],"deep":[136],"learning":[137],"CNN":[138],"comparison":[141],"other":[143],"models.":[145]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}