{"id":"https://openalex.org/W4416250186","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229033","title":"A new lightweight YOLOv8 for vehicle detection","display_name":"A new lightweight YOLOv8 for vehicle detection","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250186","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229033"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11229033","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229033","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5100323752","display_name":"Wei Wei","orcid":"https://orcid.org/0000-0002-7566-2995"},"institutions":[{"id":"https://openalex.org/I61565387","display_name":"Dalian Minzu University","ror":"https://ror.org/02hxfx521","country_code":"CN","type":"education","lineage":["https://openalex.org/I61565387"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Wei","raw_affiliation_strings":["Dalian Minzu University,School of Computer Science and Engineering,Dalian,China"],"affiliations":[{"raw_affiliation_string":"Dalian Minzu University,School of Computer Science and Engineering,Dalian,China","institution_ids":["https://openalex.org/I61565387"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113345558","display_name":"Yuxiu Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I61565387","display_name":"Dalian Minzu University","ror":"https://ror.org/02hxfx521","country_code":"CN","type":"education","lineage":["https://openalex.org/I61565387"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxiu Liu","raw_affiliation_strings":["Dalian Minzu University,School of Computer Science and Engineering,Dalian,China"],"affiliations":[{"raw_affiliation_string":"Dalian Minzu University,School of Computer Science and Engineering,Dalian,China","institution_ids":["https://openalex.org/I61565387"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101409637","display_name":"Jae Hyun Yun","orcid":"https://orcid.org/0000-0002-5264-1833"},"institutions":[{"id":"https://openalex.org/I61565387","display_name":"Dalian Minzu University","ror":"https://ror.org/02hxfx521","country_code":"CN","type":"education","lineage":["https://openalex.org/I61565387"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Yun","raw_affiliation_strings":["Dalian Minzu University,School of Computer Science and Engineering,Dalian,China"],"affiliations":[{"raw_affiliation_string":"Dalian Minzu University,School of Computer Science and Engineering,Dalian,China","institution_ids":["https://openalex.org/I61565387"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101944883","display_name":"Xiaodong Duan","orcid":"https://orcid.org/0009-0001-1775-6280"},"institutions":[{"id":"https://openalex.org/I61565387","display_name":"Dalian Minzu University","ror":"https://ror.org/02hxfx521","country_code":"CN","type":"education","lineage":["https://openalex.org/I61565387"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodong Duan","raw_affiliation_strings":["Dalian Minzu University,School of Computer Science and Engineering,Dalian,China"],"affiliations":[{"raw_affiliation_string":"Dalian Minzu University,School of Computer Science and Engineering,Dalian,China","institution_ids":["https://openalex.org/I61565387"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100323752"],"corresponding_institution_ids":["https://openalex.org/I61565387"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34320188,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9519000053405762,"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":0.9519000053405762,"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/T13918","display_name":"Advanced Data and IoT Technologies","score":0.005400000140070915,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.004800000227987766,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6704999804496765},{"id":"https://openalex.org/keywords/pascal","display_name":"Pascal (unit)","score":0.6495000123977661},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.541700005531311},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5196999907493591},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4724999964237213},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4410000145435333}],"concepts":[{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6704999804496765},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.6495000123977661},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6348999738693237},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.541700005531311},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5196999907493591},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4724999964237213},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.46320000290870667},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4410000145435333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39800000190734863},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.3952000141143799},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3199999928474426},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.30790001153945923},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.30090001225471497},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.27489998936653137},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.2705000042915344},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11229033","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229033","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2515328799","https://openalex.org/W2531409750","https://openalex.org/W2883780447","https://openalex.org/W2963125010","https://openalex.org/W2963163009","https://openalex.org/W2982083293","https://openalex.org/W3035414587","https://openalex.org/W3207115507","https://openalex.org/W4372347372","https://openalex.org/W4386047745","https://openalex.org/W4386223464","https://openalex.org/W4390874070"],"related_works":[],"abstract_inverted_index":{"The":[0,53],"technology":[1],"for":[2,18,29,58],"detecting":[3,30],"vehicles":[4],"holds":[5],"significant":[6],"importance":[7],"in":[8,74,161,170,177,183],"achieving":[9],"automated":[10],"monitoring":[11],"and":[12,50,78,96,129,148,172,200,207],"AI-assisted":[13],"driving":[14],"systems.The":[15],"advanced":[16],"technique":[17],"object":[19],"detection,":[20],"specifically":[21,56],"a":[22,45,68,117,157,166],"category":[23],"within":[24],"YOLOv8,":[25],"is":[26,55,72,85,122],"frequently":[27],"employed":[28],"vehicles.":[31],"This":[32],"paper":[33],"proposes":[34],"an":[35,79,173],"improved":[36],"lightweight":[37],"YOLOv8":[38,75,103],"detection":[39,51,90,127,162],"method":[40,54],"that":[41],"aims":[42],"to":[43,62,87,124,185],"strike":[44],"balance":[46],"between":[47],"computational":[48],"load":[49],"ratio.":[52],"designed":[57,73,123],"edge":[59],"computing":[60],"platforms":[61],"detect":[63],"vehicle.":[64],"In":[65,115],"this":[66],"method,":[67],"multi-scale":[69,81],"convolution":[70],"block":[71],"backbone":[76],"network,":[77],"efficient":[80],"attention":[82],"module":[83,121],"(EMA)":[84],"introduced":[86],"improve":[88,125],"the":[89,93,102,107,111,126,131,137,144,149,153,178,186,204],"accuracy":[91],"of":[92,109,133,180],"algorithm.":[94],"GSconv":[95],"VoVGSCSP":[97],"modules":[98],"are":[99],"incorporated":[100],"into":[101],"neck":[104],"network":[105],"with":[106,165],"aim":[108],"minimizing":[110],"floating-point":[112],"operations":[113],"(FLOPs).":[114],"addition,":[116],"new":[118],"high-speed":[119],"detector":[120],"speed":[128],"reduce":[130],"number":[132],"parameters.":[134],"To":[135],"assess":[136],"method\u2019s":[138,205],"performance,":[139],"we":[140],"conducted":[141],"experiments":[142],"using":[143],"PASCAL":[145],"VOC":[146],"dataset":[147],"MS":[150],"COCO":[151],"dataset.":[152],"proposed":[154],"model":[155,181],"exhibits":[156],"notable":[158],"3.5%":[159],"enhancement":[160],"precision,":[163],"along":[164],"substantial":[167],"42.68%":[168],"reduction":[169],"FLOPs,":[171],"impressive":[174],"30.50%":[175],"decrease":[176],"quantity":[179],"parameters":[182],"comparison":[184],"existing":[187,211],"YOLOv8.":[188],"Additionally,the":[189],"average":[190],"processing":[191],"time":[192],"has":[193],"decreased":[194],"by":[195],"25%.":[196],"Comprehensive":[197],"case":[198],"studies":[199],"comparative":[201],"analyses":[202],"demonstrate":[203],"effectiveness":[206],"its":[208],"superiority":[209],"over":[210],"approaches.":[212]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-14T00:00:00"}
