{"id":"https://openalex.org/W4414022751","doi":"https://doi.org/10.32604/cmc.2025.066803","title":"BSDNet: Semantic Information Distillation-Based for Bilateral-Branch Real-Time Semantic Segmentation on Street Scene Image","display_name":"BSDNet: Semantic Information Distillation-Based for Bilateral-Branch Real-Time Semantic Segmentation on Street Scene Image","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4414022751","doi":"https://doi.org/10.32604/cmc.2025.066803"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.066803","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.066803","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.066803","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101630130","display_name":"Huan Zeng","orcid":"https://orcid.org/0000-0002-7890-4072"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Huan Zeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101806561","display_name":"Jianxun Zhang","orcid":"https://orcid.org/0000-0003-4209-1249"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianxun Zhang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Hongji Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongji Chen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5084794187","display_name":"Xinwei Zhu","orcid":"https://orcid.org/0000-0002-4636-8893"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinwei Zhu","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101630130"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28120035,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"85","issue":"2","first_page":"3879","last_page":"3896"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.8754000067710876,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.8754000067710876,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.79339998960495,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.7753000259399414,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.6368071436882019},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6367180347442627},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5471235513687134},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.49810099601745605},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4546854496002197},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3512202203273773},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3337402641773224}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6368071436882019},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6367180347442627},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5471235513687134},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.49810099601745605},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4546854496002197},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3512202203273773},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3337402641773224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.066803","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.066803","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.066803","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.066803","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2507296351","https://openalex.org/W2963881378","https://openalex.org/W2964309882","https://openalex.org/W3191751572","https://openalex.org/W3196904463","https://openalex.org/W4312688875","https://openalex.org/W4376464632","https://openalex.org/W4396733260"],"related_works":["https://openalex.org/W4379231730","https://openalex.org/W4389858081","https://openalex.org/W2501551404","https://openalex.org/W4298131179","https://openalex.org/W2113201962","https://openalex.org/W4385583601","https://openalex.org/W4395685956","https://openalex.org/W3159516372","https://openalex.org/W4398146871","https://openalex.org/W4387803165"],"abstract_inverted_index":{"Semantic":[0,81],"segmentation":[1,58],"in":[2,33,40,45,124],"street":[3,18,67],"scenes":[4],"is":[5],"a":[6,28,38,54,74,80,87,162],"crucial":[7],"technology":[8],"for":[9,66],"autonomous":[10],"driving":[11],"to":[12,37,116],"analyze":[13],"the":[14,97,101,104,113],"surrounding":[15],"environment.":[16],"In":[17],"scenes,":[19],"issues":[20],"such":[21],"as":[22],"high":[23],"image":[24],"resolution":[25],"caused":[26],"by":[27],"large":[29],"viewpoints":[30],"and":[31,43,86,103,137,167],"differences":[32],"object":[34],"scales":[35],"lead":[36],"decline":[39],"real-time":[41,56],"performance":[42],"difficulties":[44],"multi-scale":[46,129],"feature":[47],"extraction.":[48],"To":[49],"address":[50],"this,":[51],"we":[52],"propose":[53],"bilateral-branch":[55],"semantic":[57,62,98,105,110],"method":[59,160],"based":[60],"on":[61,134,155],"information":[63,111,122],"distillation":[64],"(BSDNet)":[65],"scene":[68],"images.":[69],"The":[70],"BSDNet":[71],"consists":[72],"of":[73,142],"Feature":[75],"Conversion":[76],"Convolutional":[77],"Block":[78],"(FCB),":[79],"Information":[82],"Distillation":[83],"Module":[84],"(SIDM),":[85],"Deep":[88],"Aggregation":[89],"Atrous":[90],"Convolution":[91],"Pyramid":[92],"Pooling":[93],"(DASP).":[94],"FCB":[95],"reduces":[96],"gap":[99],"between":[100,165],"backbone":[102],"branch.":[106],"SIDM":[107],"extracts":[108],"high-quality":[109],"from":[112],"Transformer":[114],"branch":[115],"reduce":[117],"computational":[118],"costs.":[119],"DASP":[120],"aggregates":[121],"lost":[123],"atrous":[125],"convolutions,":[126],"effectively":[127],"capturing":[128],"objects.":[130],"Extensive":[131],"experiments":[132],"conducted":[133],"Cityscapes,":[135,156],"CamVid,":[136],"ADE20K,":[138],"achieving":[139],"an":[140],"accuracy":[141,166],"81.7":[143],"Mean":[144],"Intersection":[145],"over":[146],"Union":[147],"(mIoU)":[148],"at":[149],"70.6":[150],"Frames":[151],"Per":[152],"Second":[153],"(FPS)":[154],"demonstrate":[157],"that":[158],"our":[159],"achieves":[161],"better":[163],"balance":[164],"inference":[168],"speed.":[169]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
