{"id":"https://openalex.org/W7125894553","doi":"https://doi.org/10.1109/smc58881.2025.11343241","title":"MFBPNet: A Multi-Scale Fusion and Boundary Perception Network for Real-Time Semantic Segmentation in Autonomous Driving","display_name":"MFBPNet: A Multi-Scale Fusion and Boundary Perception Network for Real-Time Semantic Segmentation in Autonomous Driving","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125894553","doi":"https://doi.org/10.1109/smc58881.2025.11343241"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11343241","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343241","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5124125729","display_name":"Jiajia Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156189","display_name":"Shanghai Dianji University","ror":"https://ror.org/055fene14","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210156189"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiajia Guo","raw_affiliation_strings":["Shanghai Dianji University,School of Electronic Information,Shanghai,China,201306"],"affiliations":[{"raw_affiliation_string":"Shanghai Dianji University,School of Electronic Information,Shanghai,China,201306","institution_ids":["https://openalex.org/I4210156189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121344637","display_name":"Guangyu Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156189","display_name":"Shanghai Dianji University","ror":"https://ror.org/055fene14","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210156189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangyu Fan","raw_affiliation_strings":["Shanghai Dianji University,School of Electronic Information,Shanghai,China,201306"],"affiliations":[{"raw_affiliation_string":"Shanghai Dianji University,School of Electronic Information,Shanghai,China,201306","institution_ids":["https://openalex.org/I4210156189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103403997","display_name":"Lei Rao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156189","display_name":"Shanghai Dianji University","ror":"https://ror.org/055fene14","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210156189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Rao","raw_affiliation_strings":["Shanghai Dianji University,School of Electronic Information,Shanghai,China,201306"],"affiliations":[{"raw_affiliation_string":"Shanghai Dianji University,School of Electronic Information,Shanghai,China,201306","institution_ids":["https://openalex.org/I4210156189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121327112","display_name":"Songlin Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156189","display_name":"Shanghai Dianji University","ror":"https://ror.org/055fene14","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210156189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Songlin Cheng","raw_affiliation_strings":["Shanghai Dianji University,School of Electronic Information,Shanghai,China,201306"],"affiliations":[{"raw_affiliation_string":"Shanghai Dianji University,School of Electronic Information,Shanghai,China,201306","institution_ids":["https://openalex.org/I4210156189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121324594","display_name":"Niansheng Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156189","display_name":"Shanghai Dianji University","ror":"https://ror.org/055fene14","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210156189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Niansheng Chen","raw_affiliation_strings":["Shanghai Dianji University,School of Electronic Information,Shanghai,China,201306"],"affiliations":[{"raw_affiliation_string":"Shanghai Dianji University,School of Electronic Information,Shanghai,China,201306","institution_ids":["https://openalex.org/I4210156189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121339601","display_name":"Xiaoyong Song","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156189","display_name":"Shanghai Dianji University","ror":"https://ror.org/055fene14","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210156189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyong Song","raw_affiliation_strings":["Shanghai Dianji University,School of Electronic Information,Shanghai,China,201306"],"affiliations":[{"raw_affiliation_string":"Shanghai Dianji University,School of Electronic Information,Shanghai,China,201306","institution_ids":["https://openalex.org/I4210156189"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030881704","display_name":"Dingyu Yang","orcid":"https://orcid.org/0000-0002-8156-3926"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dingyu Yang","raw_affiliation_strings":["Zhejiang University,State Key Laboratory of Blockchain and Data Security,Zhejiang,China,310058"],"affiliations":[{"raw_affiliation_string":"Zhejiang University,State Key Laboratory of Blockchain and Data Security,Zhejiang,China,310058","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5124125729"],"corresponding_institution_ids":["https://openalex.org/I4210156189"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.68419984,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1677","last_page":"1682"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.8747000098228455,"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":0.8747000098228455,"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":0.08380000293254852,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.006500000134110451,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/segmentation","display_name":"Segmentation","score":0.7609999775886536},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5648000240325928},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.462799996137619},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44110000133514404},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4108000099658966},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.4106999933719635},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3970000147819519},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.3621000051498413},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.3481999933719635}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.789900004863739},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7728000283241272},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7609999775886536},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6079000234603882},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5648000240325928},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.462799996137619},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44110000133514404},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4108000099658966},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.4106999933719635},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3970000147819519},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3621000051498413},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3481999933719635},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.3391000032424927},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3357999920845032},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3285999894142151},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.30720001459121704},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.26899999380111694},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11343241","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343241","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7234379053115845}],"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":27,"referenced_works":["https://openalex.org/W2109255472","https://openalex.org/W2560023338","https://openalex.org/W2565639579","https://openalex.org/W2762439315","https://openalex.org/W2763029081","https://openalex.org/W2799166040","https://openalex.org/W2886934227","https://openalex.org/W2963890956","https://openalex.org/W2964309882","https://openalex.org/W2971198903","https://openalex.org/W3008115128","https://openalex.org/W3035665735","https://openalex.org/W3082097505","https://openalex.org/W3100047268","https://openalex.org/W3128892380","https://openalex.org/W3169865585","https://openalex.org/W3190437803","https://openalex.org/W3196904463","https://openalex.org/W3215565096","https://openalex.org/W4285144577","https://openalex.org/W4312688875","https://openalex.org/W4367051569","https://openalex.org/W4386824854","https://openalex.org/W4387448076","https://openalex.org/W4390661108","https://openalex.org/W4396909812","https://openalex.org/W4399863453"],"related_works":[],"abstract_inverted_index":{"Semantic":[0],"segmentation":[1,17,23,46,166,185],"is":[2],"crucial":[3],"in":[4,8,14,36],"practical":[5],"applications,":[6],"especially":[7],"autonomous":[9,176],"driving.":[10],"Despite":[11],"significant":[12],"advancements":[13],"existing":[15,158],"semantic":[16,45],"methods,":[18,38,159],"the":[19,29,58,67,77,97,106,120],"performance":[20],"of":[21,109,122],"real-time":[22,44,169,181],"approaches":[24],"remains":[25],"suboptimal.":[26],"To":[27],"address":[28],"trade-off":[30],"between":[31,165],"computational":[32],"efficiency":[33],"and":[34,71,95,125,134,149,168,183],"accuracy":[35,167],"current":[37],"we":[39],"propose":[40],"a":[41,113,162],"novel":[42],"lightweight":[43],"network":[47],"named":[48],"MFBPNet.":[49],"Specifically,":[50],"this":[51],"paper":[52],"introduces":[53],"three":[54],"core":[55],"modules:":[56],"(1)":[57],"Depthwise":[59],"Separable":[60],"Convolutional":[61],"Pyramid":[62],"Module":[63,81,102],"(DSCPM),":[64],"which":[65,104],"expands":[66],"global":[68],"receptive":[69],"field":[70],"enhances":[72],"deep":[73],"feature":[74,89,107,115],"representation;":[75],"(2)":[76],"Local":[78],"Attention":[79],"Refinement":[80],"(LARM),":[82],"employing":[83],"channel-wise":[84],"attention":[85],"to":[86,157],"refine":[87],"local":[88],"discriminability,":[90],"particularly":[91],"for":[92,175],"fine-grained":[93],"objects;":[94],"(3)":[96],"Boundary":[98],"Perception":[99],"Feature":[100],"Fusion":[101],"(BPFM),":[103],"strengthens":[105],"representation":[108],"boundary":[110,127],"regions":[111],"through":[112],"multi-level":[114],"fusion":[116],"mechanism,":[117],"effectively":[118],"enhancing":[119],"clarity":[121],"object":[123],"boundaries":[124],"mitigating":[126],"blurring":[128],"issues.":[129],"Extensive":[130],"experiments":[131],"on":[132],"Cityscapes":[133],"CamVid":[135],"datasets":[136],"demonstrate":[137],"that":[138],"MFBPNet":[139,160],"achieves":[140,161],"state-of-the-art":[141],"performance,":[142,170],"attaining":[143],"75.3%":[144],"mIoU":[145,151],"at":[146,152],"67.3":[147],"FPS":[148],"74.3%":[150],"68.2":[153],"FPS,":[154],"respectively.":[155],"Compared":[156],"superior":[163],"balance":[164],"rendering":[171],"it":[172],"highly":[173],"suitable":[174],"driving":[177],"systems":[178],"requiring":[179],"both":[180],"processing":[182],"high":[184],"quality.":[186]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-29T00:00:00"}