{"id":"https://openalex.org/W7133232580","doi":"https://doi.org/10.1109/tits.2026.3665257","title":"YOLO-LIGHT: A Real-Time Lightweight Model Based on Limited Edge Computing for Traffic Scene Detection and Tracking","display_name":"YOLO-LIGHT: A Real-Time Lightweight Model Based on Limited Edge Computing for Traffic Scene Detection and Tracking","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133232580","doi":"https://doi.org/10.1109/tits.2026.3665257"},"language":null,"primary_location":{"id":"doi:10.1109/tits.2026.3665257","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2026.3665257","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","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/A5127840431","display_name":"Quan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quan Wang","raw_affiliation_strings":["School of Internet of Things Engineering, Wuxi University, Wuxi, China"],"raw_orcid":"https://orcid.org/0000-0003-0258-7629","affiliations":[{"raw_affiliation_string":"School of Internet of Things Engineering, Wuxi University, Wuxi, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Guangfei Ye","orcid":"https://orcid.org/0009-0000-0188-1231"},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangfei Ye","raw_affiliation_strings":["School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0000-0188-1231","affiliations":[{"raw_affiliation_string":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I200845125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127853455","display_name":"Fu Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu Li","raw_affiliation_strings":["School of Internet of Things Engineering, Wuxi University, Wuxi, China"],"raw_orcid":"https://orcid.org/0009-0007-9055-0960","affiliations":[{"raw_affiliation_string":"School of Internet of Things Engineering, Wuxi University, Wuxi, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101668036","display_name":"Qidong Chen","orcid":"https://orcid.org/0000-0002-2116-1108"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qidong Chen","raw_affiliation_strings":["School of Internet of Things Engineering, Wuxi University, Wuxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Internet of Things Engineering, Wuxi University, Wuxi, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036079804","display_name":"J. F. Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I125749732","display_name":"Western University","ror":"https://ror.org/02grkyz14","country_code":"CA","type":"education","lineage":["https://openalex.org/I125749732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jin Jiang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Faculty of Engineering, University of Western Ontario, London, ON, Canada"],"raw_orcid":"https://orcid.org/0000-0001-7078-5999","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Faculty of Engineering, University of Western Ontario, London, ON, Canada","institution_ids":["https://openalex.org/I125749732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100747794","display_name":"Songyang Zhang","orcid":"https://orcid.org/0000-0003-4316-3320"},"institutions":[{"id":"https://openalex.org/I308837","display_name":"Suzhou University of Science and Technology","ror":"https://ror.org/04en8wb91","country_code":"CN","type":"education","lineage":["https://openalex.org/I308837"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Songyang Zhang","raw_affiliation_strings":["School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu, China","institution_ids":["https://openalex.org/I308837"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5127820701","display_name":"Farhan Ullah","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Farhan Ullah","raw_affiliation_strings":["School of Internet of Things Engineering, Wuxi University, Wuxi, China"],"raw_orcid":"https://orcid.org/0000-0001-7971-7992","affiliations":[{"raw_affiliation_string":"School of Internet of Things Engineering, Wuxi University, Wuxi, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.8969,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.96611075,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"27","issue":"6","first_page":"6874","last_page":"6889"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.3495999872684479,"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":0.3495999872684479,"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":0.15309999883174896,"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"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.05620000138878822,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.5687000155448914},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5210000276565552},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.517300009727478},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.505299985408783},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4772000014781952},{"id":"https://openalex.org/keywords/edge-detection","display_name":"Edge detection","score":0.46129998564720154},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.4580000042915344},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.44119998812675476},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4178999960422516},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4147000014781952}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7282999753952026},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.5687000155448914},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5210000276565552},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.517300009727478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5170000195503235},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.505299985408783},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4772000014781952},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.46129998564720154},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.4580000042915344},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4578000009059906},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.44119998812675476},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4178999960422516},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4171000123023987},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4147000014781952},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.4146000146865845},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4049000144004822},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.3889999985694885},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.384799987077713},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.37389999628067017},{"id":"https://openalex.org/C84119951","wikidata":"https://www.wikidata.org/wiki/Q3498530","display_name":"Vehicle tracking system","level":3,"score":0.3675999939441681},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.35740000009536743},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.34940001368522644},{"id":"https://openalex.org/C154586513","wikidata":"https://www.wikidata.org/wiki/Q4420972","display_name":"Tracking system","level":3,"score":0.31450000405311584},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.2851000130176544},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.26510000228881836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2026.3665257","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2026.3665257","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1841836965","display_name":null,"funder_award_id":"2022r043","funder_id":"https://openalex.org/F4320328245","funder_display_name":"National University Research Fund of China"}],"funders":[{"id":"https://openalex.org/F4320328245","display_name":"National University Research Fund of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Intelligent":[0],"transportation":[1],"systems":[2],"(ITSs)":[3],"play":[4],"a":[5,31,50,57,75,143,183,200,205],"vital":[6],"role":[7],"in":[8,79,89],"addressing":[9],"urban":[10],"traffic":[11],"safety":[12],"challenges.":[13],"The":[14],"deployment":[15],"of":[16,40,103,122,187],"lightweight":[17,58],"and":[18,25,55,67,86,97,114,147,181,204],"efficient":[19],"algorithms":[20],"for":[21,212],"conducting":[22],"vehicle":[23,52,162],"detection":[24,53,81,128,140],"tracking":[26,59,131],"on":[27,135,167,192],"edge":[28],"devices":[29],"is":[30,155],"key":[32],"requirement,":[33],"due":[34],"to":[35,106,118,158],"the":[36,63,69,84,90,101,104,120,123,168,176],"limited":[37],"computational":[38,71,184],"resources":[39],"these":[41],"devices.":[42],"To":[43],"address":[44],"this":[45],"challenge,":[46],"we":[47],"propose":[48],"YOLO-LIGHT,":[49],"real-time":[51,213],"model,":[54],"Ve-Track,":[56],"algorithm.":[60],"YOLO-LIGHT":[61,174],"improves":[62,175],"feature":[64,94],"extraction":[65],"process":[66],"reduces":[68],"incurred":[70],"cost.":[72],"It":[73],"integrates":[74],"novel":[76],"FastPConv":[77],"convolution":[78],"its":[80,127,210],"head,":[82],"while":[83,125],"DySample":[85],"SCAM":[87],"modules":[88],"neck":[91],"provide":[92],"enhanced":[93,151],"representations.":[95],"Soft-NMS":[96],"AMA_SPPF":[98],"further":[99],"strengthen":[100],"ability":[102],"model":[105,124,160],"capture":[107],"fine":[108],"details.":[109],"Additionally,":[110],"global":[111],"channel":[112],"sparsity":[113],"pruning":[115],"are":[116],"employed":[117],"compress":[119],"parameters":[121],"preserving":[126],"accuracy.":[129],"For":[130],"purposes,":[132],"Ve-Track":[133,196],"builds":[134],"ByteTrack":[136],"by":[137,179],"filtering":[138],"background":[139],"boxes":[141],"using":[142],"\u201clife":[144],"value\u201d":[145],"indicator":[146],"recalculating":[148],"scores.":[149],"An":[150],"Kalman":[152],"filter":[153],"(EV-KF)":[154],"also":[156],"introduced":[157],"better":[159],"nonlinear":[161],"motions.":[163],"Experimental":[164],"results":[165],"obtained":[166],"modified":[169],"VisDrone":[170],"dataset":[171],"show":[172],"that":[173],"mAP95":[177],"metric":[178],"17.6%":[180],"exhibits":[182],"complexity":[185],"reduction":[186],"82.6%,":[188],"achieving":[189],"51":[190],"FPS":[191,207],"an":[193],"embedded":[194],"platform.":[195],"outperforms":[197],"ByteTrack,":[198],"with":[199],"6.2%":[201],"MOTA":[202],"increase":[203],"17.2%":[206],"increase,":[208],"demonstrating":[209],"suitability":[211],"ITS":[214],"applications.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-05T06:17:00.636019","created_date":"2026-03-03T00:00:00"}