{"id":"https://openalex.org/W4410582309","doi":"https://doi.org/10.1109/access.2025.3572331","title":"YOLOP-MVF: A Multi-Task Autonomous Driving Perception Detection Method Based on Multi Scale Feature Weighted Fusion","display_name":"YOLOP-MVF: A Multi-Task Autonomous Driving Perception Detection Method Based on Multi Scale Feature Weighted Fusion","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4410582309","doi":"https://doi.org/10.1109/access.2025.3572331"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3572331","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3572331","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.2025.3572331","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108247328","display_name":"Yanqiu Niu","orcid":null},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanqiu Niu","raw_affiliation_strings":["Department of Basic Sciences, Jilin University of Architecture and Technology, Changchun, China"],"raw_orcid":"https://orcid.org/0009-0009-8422-8988","affiliations":[{"raw_affiliation_string":"Department of Basic Sciences, Jilin University of Architecture and Technology, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101994915","display_name":"Jing Zhang","orcid":"https://orcid.org/0000-0002-7531-7032"},"institutions":[{"id":"https://openalex.org/I4210114245","display_name":"Anhui Business College","ror":"https://ror.org/02d0cgn19","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210114245"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Zhang","raw_affiliation_strings":["Department of Public Instruction, Anhui Vocational College of City Management, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0002-7531-7032","affiliations":[{"raw_affiliation_string":"Department of Public Instruction, Anhui Vocational College of City Management, Hefei, China","institution_ids":["https://openalex.org/I4210114245"]}]}],"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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07947556,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"91374","last_page":"91383"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9692000150680542,"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":0.9692000150680542,"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":0.9398999810218811,"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":0.9157999753952026,"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/computer-science","display_name":"Computer science","score":0.7527053356170654},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.634272575378418},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6045792698860168},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5706489086151123},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.533238410949707},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5185676217079163},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5033597350120544},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4861690402030945},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4500758945941925},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.402086466550827},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08185353875160217}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7527053356170654},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.634272575378418},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6045792698860168},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5706489086151123},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.533238410949707},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5185676217079163},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5033597350120544},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4861690402030945},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4500758945941925},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.402086466550827},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08185353875160217},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3572331","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3572331","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:83b3692788524cfd8de990398c3e08de","is_oa":true,"landing_page_url":"https://doaj.org/article/83b3692788524cfd8de990398c3e08de","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 91374-91383 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3572331","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3572331","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":[{"display_name":"Gender equality","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/5"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2059002724","https://openalex.org/W2109255472","https://openalex.org/W2124802015","https://openalex.org/W2144707081","https://openalex.org/W2571175805","https://openalex.org/W2593539516","https://openalex.org/W2748263833","https://openalex.org/W2779226918","https://openalex.org/W2780740184","https://openalex.org/W2913960518","https://openalex.org/W2963037989","https://openalex.org/W3034971973","https://openalex.org/W3113929572","https://openalex.org/W3157173860","https://openalex.org/W3175091786","https://openalex.org/W3176566042","https://openalex.org/W3185598201","https://openalex.org/W4225742096","https://openalex.org/W4299304994","https://openalex.org/W4308450151","https://openalex.org/W4312807693","https://openalex.org/W4389538039","https://openalex.org/W4393394987","https://openalex.org/W6688093443","https://openalex.org/W6947681574"],"related_works":["https://openalex.org/W3147584709","https://openalex.org/W2099421762","https://openalex.org/W2530546662","https://openalex.org/W2967030268","https://openalex.org/W2977677679","https://openalex.org/W2185253430","https://openalex.org/W2132659060","https://openalex.org/W2031992971","https://openalex.org/W3214791684","https://openalex.org/W2152662039"],"abstract_inverted_index":{"To":[0,65],"address":[1],"challenges":[2],"such":[3],"as":[4],"large-scale":[5],"variations,":[6],"background":[7],"interference,":[8],"and":[9,38,95,121],"occlusions":[10],"in":[11,116,119,123,132],"multi-task":[12,21,130],"autonomous":[13],"driving":[14,134],"perception,":[15],"this":[16],"paper":[17],"proposes":[18],"YOLOP-MVF,":[19],"a":[20,33,39],"detection":[22],"framework":[23],"based":[24,77],"on":[25,78,102],"multi-scale":[26,49,52],"feature":[27,41,67],"weighting":[28],"fusion.":[29],"The":[30],"model":[31],"integrates":[32],"sub-pixel":[34],"3D":[35],"fusion":[36],"module":[37,43],"triple":[40],"encoding":[42],"to":[44,59,87],"enhance":[45],"the":[46,82,103],"representation":[47],"of":[48,114],"features.":[50],"A":[51],"convolutional":[53],"attention-weighting":[54],"mechanism":[55],"is":[56,85],"further":[57],"introduced":[58],"adaptively":[60],"emphasize":[61],"critical":[62],"spatial":[63],"information.":[64],"improve":[66],"extraction":[68],"flexibility,":[69],"deformable":[70],"convolutions":[71],"are":[72],"incorporated,":[73],"enabling":[74],"dynamic":[75],"sampling":[76],"input":[79],"characteristics.":[80],"Additionally,":[81],"Powerful-IoU":[83],"loss":[84],"employed":[86],"guide":[88],"anchor":[89],"box":[90],"regression":[91],"with":[92],"adaptive":[93],"penalty":[94],"gradient":[96],"regulation,":[97],"accelerating":[98],"convergence.":[99],"Experimental":[100],"results":[101],"BDD100K":[104],"dataset":[105],"demonstrate":[106],"that":[107],"YOLOP-MVF":[108],"outperforms":[109],"baseline":[110],"models,":[111],"achieving":[112],"improvements":[113],"1.2%":[115],"mIoU,":[117],"8.8%":[118],"accuracy,":[120],"4.7%":[122],"mAP50,":[124],"validating":[125],"its":[126],"effectiveness":[127],"for":[128],"robust":[129],"perception":[131],"complex":[133],"scenarios.":[135]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
