{"id":"https://openalex.org/W4406857539","doi":"https://doi.org/10.1109/access.2025.3534321","title":"VRU-YOLO: A Small Object Detection Algorithm for Vulnerable Road Users in Complex Scenes","display_name":"VRU-YOLO: A Small Object Detection Algorithm for Vulnerable Road Users in Complex Scenes","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4406857539","doi":"https://doi.org/10.1109/access.2025.3534321"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3534321","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3534321","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2025.3534321","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103035542","display_name":"Yunxiang Liu","orcid":"https://orcid.org/0009-0007-0478-3371"},"institutions":[{"id":"https://openalex.org/I67001856","display_name":"Shanghai Institute of Technology","ror":"https://ror.org/00fjzqj15","country_code":"CN","type":"education","lineage":["https://openalex.org/I67001856"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunxiang Liu","raw_affiliation_strings":["School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China","institution_ids":["https://openalex.org/I67001856"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100952460","display_name":"Yuqing Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I67001856","display_name":"Shanghai Institute of Technology","ror":"https://ror.org/00fjzqj15","country_code":"CN","type":"education","lineage":["https://openalex.org/I67001856"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuqing Shi","raw_affiliation_strings":["School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0003-3865-9388","affiliations":[{"raw_affiliation_string":"School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China","institution_ids":["https://openalex.org/I67001856"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":6.5446,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.96470495,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"13","issue":null,"first_page":"19996","last_page":"20015"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.982699990272522,"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":0.982699990272522,"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":0.9613000154495239,"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":0.9610000252723694,"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/computer-science","display_name":"Computer science","score":0.7390909790992737},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5826705098152161},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5346848964691162},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4917486011981964},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4259738624095917},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4032590687274933},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.1075439453125}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7390909790992737},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5826705098152161},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5346848964691162},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4917486011981964},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4259738624095917},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4032590687274933},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.1075439453125}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3534321","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3534321","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:00eefacd5915419a89f8ad9f57ab1119","is_oa":true,"landing_page_url":"https://doaj.org/article/00eefacd5915419a89f8ad9f57ab1119","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 19996-20015 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3534321","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3534321","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1861492603","https://openalex.org/W2102605133","https://openalex.org/W2120419212","https://openalex.org/W2150066425","https://openalex.org/W2161969291","https://openalex.org/W2164598857","https://openalex.org/W2340897893","https://openalex.org/W2513907769","https://openalex.org/W2565639579","https://openalex.org/W2618530766","https://openalex.org/W2962927175","https://openalex.org/W2963037989","https://openalex.org/W2963857746","https://openalex.org/W2990763144","https://openalex.org/W3018757597","https://openalex.org/W3035564946","https://openalex.org/W3042011474","https://openalex.org/W3092663126","https://openalex.org/W3096609285","https://openalex.org/W3138516171","https://openalex.org/W3208645658","https://openalex.org/W4205276794","https://openalex.org/W4214700196","https://openalex.org/W4221041279","https://openalex.org/W4283688235","https://openalex.org/W4320712808","https://openalex.org/W4323318524","https://openalex.org/W4327652243","https://openalex.org/W4372347372","https://openalex.org/W4382450226","https://openalex.org/W4384652394","https://openalex.org/W4386076325","https://openalex.org/W4386319016","https://openalex.org/W4387653441","https://openalex.org/W4388915117","https://openalex.org/W4391216231","https://openalex.org/W4391697017","https://openalex.org/W4392503863","https://openalex.org/W4393150006","https://openalex.org/W4399330756","https://openalex.org/W4399665926","https://openalex.org/W6750227808","https://openalex.org/W6784094891","https://openalex.org/W6798838024","https://openalex.org/W6847876731","https://openalex.org/W6849535052","https://openalex.org/W6862000706","https://openalex.org/W6868582632"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Accurate":[0],"detection":[1,28,50,131,235,242],"of":[2,79,138,163,219],"vulnerable":[3],"road":[4,245],"users":[5],"(VRUs)":[6],"is":[7,93,124],"critical":[8],"for":[9,240],"enhancing":[10],"traffic":[11],"safety":[12],"and":[13,24,34,63,76,145,184,190],"advancing":[14],"autonomous":[15],"driving":[16],"systems.":[17],"However,":[18],"due":[19],"to":[20,31,72,134,195],"their":[21],"small":[22,80,113,209,233],"size":[23],"unpredictable":[25],"movements,":[26],"existing":[27],"methods":[29],"struggle":[30],"provide":[32],"stable":[33],"accurate":[35],"results":[36,168],"under":[37],"real-time":[38],"conditions.":[39],"To":[40],"overcome":[41],"these":[42],"challenges,":[43],"this":[44],"paper":[45],"proposes":[46],"an":[47,217],"improved":[48,226],"VRU":[49,173,241],"algorithm":[51],"based":[52,104],"on":[53,105,169,205],"YOLOv8,":[54],"named":[55],"VRU-YOLO.":[56],"First,":[57],"we":[58,148],"redesign":[59],"the":[60,74,84,136,150,161,177,196,206,213,225],"neck":[61],"structure":[62],"construct":[64],"a":[65,96,117,170,201],"Detail":[66],"Enhancement":[67],"Feature":[68,98],"Pyramid":[69,88,99],"Network":[70],"(DEFPN)":[71],"enhance":[73],"extraction":[75],"fusion":[77],"capabilities":[78],"target":[81,114,164,210],"features.":[82],"Second,":[83],"YOLOv8":[85],"network\u2019s":[86],"Spatial":[87],"Pooling":[89],"Fast":[90,101],"(SPPF)":[91],"module":[92,103],"replaced":[94],"with":[95],"novel":[97],"Convolution":[100],"(FPCF)":[102],"dilated":[106],"convolution,":[107],"effectively":[108],"mitigating":[109],"feature":[110],"loss":[111,140,156],"in":[112,142,193,200,232,243],"processing.":[115],"Additionally,":[116],"lightweight":[118],"Optimized":[119],"Shared":[120],"Detection":[121],"Head":[122],"(OSDH-Head)":[123],"introduced,":[125],"reducing":[126],"computational":[127,146],"complexity":[128],"while":[129],"improving":[130],"efficiency.":[132],"Finally,":[133],"alleviate":[135],"deficiencies":[137],"traditional":[139],"functions":[141],"shape":[143],"matching":[144],"efficiency,":[147],"propose":[149],"Wise-Powerful":[151],"Intersection":[152],"over":[153],"Union":[154],"(WPIoU)":[155],"function,":[157],"which":[158],"further":[159],"optimizes":[160],"regression":[162],"bounding":[165],"boxes.":[166],"Experimental":[167],"custom-built":[171],"multi-source":[172],"dataset":[174,211],"show":[175],"that":[176,224],"proposed":[178],"model":[179,215,227],"enhances":[180],"precision,":[181],"recall,":[182],"mAP50,":[183],"mAP50:95":[185],"by":[186],"1.3%,":[187],"3.4%,":[188],"3.3%,":[189],"1.8%,":[191],"respectively,":[192],"comparison":[194],"baseline":[197],"model.":[198],"Moreover,":[199],"generalization":[202],"test":[203],"conducted":[204],"remote":[207],"sensing":[208],"VisDrone2019,":[212],"VRU-YOLO":[214],"achieved":[216],"mAP50":[218],"31%.":[220],"This":[221],"study":[222],"demonstrates":[223],"offers":[228],"more":[229],"efficient":[230],"performance":[231],"object":[234],"scenarios,":[236],"making":[237],"it":[238],"well-suited":[239],"complex":[244],"environments.":[246]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
