{"id":"https://openalex.org/W7127323293","doi":"https://doi.org/10.1109/access.2026.3659918","title":"RFAM-YOLO: Reinforced Feature Aggregation and Multi-Attention YOLOv8s for High-Precision Traffic Sign Detection","display_name":"RFAM-YOLO: Reinforced Feature Aggregation and Multi-Attention YOLOv8s for High-Precision Traffic Sign Detection","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7127323293","doi":"https://doi.org/10.1109/access.2026.3659918"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3659918","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3659918","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3659918","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084626487","display_name":"L. Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153869","display_name":"Huaiyin Institute of Technology","ror":"https://ror.org/0555ezg60","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153869"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Le Wang","raw_affiliation_strings":["Faculty of Automation, Huaiyin Institute of Technology, Huai’an, China"],"raw_orcid":"https://orcid.org/0000-0002-3320-0848","affiliations":[{"raw_affiliation_string":"Faculty of Automation, Huaiyin Institute of Technology, Huai’an, China","institution_ids":["https://openalex.org/I4210153869"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124903787","display_name":"Ziqi Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153869","display_name":"Huaiyin Institute of Technology","ror":"https://ror.org/0555ezg60","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153869"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziqi Jiang","raw_affiliation_strings":["Faculty of Automation, Huaiyin Institute of Technology, Huai’an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Automation, Huaiyin Institute of Technology, Huai’an, China","institution_ids":["https://openalex.org/I4210153869"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chuang Chen","orcid":"https://orcid.org/0000-0003-2117-7860"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuang Chen","raw_affiliation_strings":["Department of Automation, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-2117-7860","affiliations":[{"raw_affiliation_string":"Department of Automation, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083237430","display_name":"Hongbiao Zhou","orcid":"https://orcid.org/0000-0003-2924-9937"},"institutions":[{"id":"https://openalex.org/I4210153869","display_name":"Huaiyin Institute of Technology","ror":"https://ror.org/0555ezg60","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153869"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongbiao Zhou","raw_affiliation_strings":["Faculty of Automation, Huaiyin Institute of Technology, Huai’an, China"],"raw_orcid":"https://orcid.org/0000-0003-2924-9937","affiliations":[{"raw_affiliation_string":"Faculty of Automation, Huaiyin Institute of Technology, Huai’an, China","institution_ids":["https://openalex.org/I4210153869"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13419862,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"18509","last_page":"18529"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9688000082969666,"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":0.9688000082969666,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.004100000020116568,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.0024999999441206455,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/robustness","display_name":"Robustness (evolution)","score":0.70169997215271},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6582000255584717},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.6327999830245972},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.5839999914169312},{"id":"https://openalex.org/keywords/traffic-sign","display_name":"Traffic sign","score":0.554099977016449},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5256999731063843},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5135999917984009},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.483599990606308},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4523000121116638},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4413999915122986}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8047999739646912},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.70169997215271},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6582000255584717},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.6327999830245972},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.5839999914169312},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5644000172615051},{"id":"https://openalex.org/C2983860417","wikidata":"https://www.wikidata.org/wiki/Q170285","display_name":"Traffic sign","level":3,"score":0.554099977016449},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5256999731063843},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5135999917984009},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.483599990606308},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4523000121116638},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4413999915122986},{"id":"https://openalex.org/C6528762","wikidata":"https://www.wikidata.org/wiki/Q1574298","display_name":"Traffic sign recognition","level":4,"score":0.42660000920295715},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.42329999804496765},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42260000109672546},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.382099986076355},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.36500000953674316},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34869998693466187},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.329800009727478},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.31349998712539673},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31299999356269836},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3659918","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3659918","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:7643ee7c586744289ae2ab7a94b61d62","is_oa":false,"landing_page_url":"https://doaj.org/article/7643ee7c586744289ae2ab7a94b61d62","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 18509-18529 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3659918","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3659918","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G12040165","display_name":null,"funder_award_id":"62303217","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2354241179","display_name":null,"funder_award_id":"24KJB550003","funder_id":"https://openalex.org/F4320335440","funder_display_name":"Natural Science Research of Jiangsu Higher Education Institutions of China"},{"id":"https://openalex.org/G2402988741","display_name":null,"funder_award_id":"24KJB510004","funder_id":"https://openalex.org/F4320335440","funder_display_name":"Natural Science Research of Jiangsu Higher Education Institutions of China"},{"id":"https://openalex.org/G3503278028","display_name":null,"funder_award_id":"24KJB470002","funder_id":"https://openalex.org/F4320335440","funder_display_name":"Natural Science Research of Jiangsu Higher Education Institutions of China"},{"id":"https://openalex.org/G7374999895","display_name":null,"funder_award_id":"25KJD120001","funder_id":"https://openalex.org/F4320335440","funder_display_name":"Natural Science Research of Jiangsu Higher Education Institutions of China"},{"id":"https://openalex.org/G7646600254","display_name":null,"funder_award_id":"62503189","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335440","display_name":"Natural Science Research of Jiangsu Higher Education Institutions of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2295198236","https://openalex.org/W2570343428","https://openalex.org/W2613959248","https://openalex.org/W2940955785","https://openalex.org/W2963037989","https://openalex.org/W2975256828","https://openalex.org/W2990763144","https://openalex.org/W3010850948","https://openalex.org/W3018757597","https://openalex.org/W3108566774","https://openalex.org/W3111897001","https://openalex.org/W4200221062","https://openalex.org/W4210667216","https://openalex.org/W4212809080","https://openalex.org/W4224247488","https://openalex.org/W4282919254","https://openalex.org/W4285176535","https://openalex.org/W4288045170","https://openalex.org/W4312337285","https://openalex.org/W4312661380","https://openalex.org/W4362631162","https://openalex.org/W4386076325","https://openalex.org/W4387092319","https://openalex.org/W4389371100","https://openalex.org/W4389831849","https://openalex.org/W4390871580","https://openalex.org/W4392106972","https://openalex.org/W4392827296","https://openalex.org/W4400625252","https://openalex.org/W4401056373","https://openalex.org/W4401567514","https://openalex.org/W4402497193","https://openalex.org/W4402529054","https://openalex.org/W4402593093","https://openalex.org/W4404039299","https://openalex.org/W4404049374","https://openalex.org/W4404740748","https://openalex.org/W4405079606","https://openalex.org/W4407856405","https://openalex.org/W4410393780"],"related_works":[],"abstract_inverted_index":{"In":[0],"the":[1,25,32,55,72,92,97,114,152,187,201,228,243],"field":[2],"of":[3,35,99,116],"intelligent":[4],"driving,":[5,10],"including":[6],"autonomous":[7],"and":[8,31,67,134,147,166,177,196,225,251,256],"assisted":[9],"traffic":[11,36,44,119,173,258],"sign":[12,45,174],"detection":[13,46,115,124,167,184],"is":[14,89,127,157],"critical,":[15],"as":[16],"its":[17],"performance":[18,185],"directly":[19],"affects":[20],"vehicle":[21],"safety.":[22],"To":[23],"address":[24],"challenges":[26],"posed":[27],"by":[28],"complex":[29],"backgrounds":[30],"small":[33,118,255],"size":[34,195],"signs,":[37,120],"this":[38],"paper":[39],"proposes":[40],"an":[41,121],"improved":[42,252],"small-object":[43,123],"algorithm":[47],"based":[48],"on":[49,170],"YOLOv8s,":[50],"named":[51],"RFAM-YOLO.":[52],"First,":[53],"since":[54],"shared":[56],"parameter":[57],"mechanism":[58],"in":[59,142,211,223,260],"standard":[60],"convolution":[61,77],"kernels":[62],"can":[63],"limit":[64],"performance,":[65],"RFCAConv":[66],"C2f_RFCAConv":[68],"are":[69],"integrated":[70],"into":[71,91],"backbone":[73],"network":[74],"to":[75,95,112,129,138,218,237,254],"enhance":[76,96,113],"operations,":[78],"thereby":[79,107],"improving":[80,108,161],"multi-scale":[81],"feature":[82,109],"extraction.":[83],"Second,":[84],"a":[85,192,206,247],"C2f_MLCA":[86],"attention":[87],"module":[88,126],"incorporated":[90],"Neck":[93],"component":[94],"representation":[98],"key":[100],"object":[101],"features":[102],"while":[103,190],"maintaining":[104,191],"computational":[105],"efficiency,":[106],"fusion.":[110],"Third,":[111],"multiple":[117],"additional":[122],"head":[125],"introduced":[128],"better":[130],"capture":[131],"their":[132],"characteristics":[133],"contextual":[135],"information.":[136],"Finally,":[137],"account":[139],"for":[140],"differences":[141],"aspect":[143],"ratios":[144],"between":[145],"predicted":[146],"ground":[148],"truth":[149],"bounding":[150,162],"boxes,":[151],"original":[153],"CIoU":[154],"loss":[155],"function":[156],"replaced":[158],"with":[159,220],"Focaler-ShapeIoU,":[160],"box":[163],"regression":[164],"accuracy":[165],"robustness.":[168],"Experiments":[169],"two":[171],"benchmark":[172],"datasets,":[175],"TT100K":[176,202],"CCTSDB2021,":[178],"demonstrate":[179],"that":[180,242],"RFAM-YOLO":[181,204],"consistently":[182],"improves":[183],"over":[186],"YOLOv8s":[188],"baseline":[189],"lightweight":[193],"model":[194],"real-time":[197],"inference":[198],"speed.":[199],"On":[200,227],"dataset,":[203,230],"achieves":[205],"4.38":[207],"percentage":[208],"point":[209],"increase":[210],"mean":[212],"Average":[213],"Precision":[214],"(mAP@50),":[215],"from":[216,235],"86.09%":[217],"90.47%,":[219],"simultaneous":[221],"gains":[222],"precision":[224],"recall.":[226],"CCTSDB2021":[229],"it":[231],"further":[232],"boosts":[233],"mAP@50":[234],"96.54%":[236],"98.20%.":[238],"These":[239],"results":[240],"show":[241],"proposed":[244],"architecture":[245],"provides":[246],"favorable":[248],"accuracy-efficiency":[249],"trade-off":[250],"robustness":[253],"cluttered":[257],"signs":[259],"realistic":[261],"driving":[262],"scenarios.":[263]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-04T00:00:00"}