{"id":"https://openalex.org/W7225460359","doi":"https://doi.org/10.1007/s44163-026-02588-z","title":"Collaborative object detection and semantic segmentation algorithm integrating lightweight deep learning for complex traffic scenes","display_name":"Collaborative object detection and semantic segmentation algorithm integrating lightweight deep learning for complex traffic scenes","publication_year":2026,"publication_date":"2026-11-07","ids":{"openalex":"https://openalex.org/W7225460359","doi":"https://doi.org/10.1007/s44163-026-02588-z"},"language":"en","primary_location":{"id":"doi:10.1007/s44163-026-02588-z","doi":"https://doi.org/10.1007/s44163-026-02588-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-02588-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-026-02588-z.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1","oarl"],"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1007/s44163-026-02588-z.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161199002","display_name":"Hongjiang Liu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210108633","display_name":"Aba Teachers University","ror":"https://ror.org/01gqdjc98","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210108633"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongjiang Liu","raw_affiliation_strings":["School of Computer Science and Technology, Aba Teachers College, Wenchuan, 623002, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Aba Teachers College, Wenchuan, 623002, China","institution_ids":["https://openalex.org/I4210108633"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5161199002"],"corresponding_institution_ids":["https://openalex.org/I4210108633"],"apc_list":{"value":1590,"currency":"USD","value_usd":1590},"apc_paid":{"value":1590,"currency":"USD","value_usd":1590},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"6","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":9.999999747378752e-05,"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"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":9.999999747378752e-05,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/object-detection","display_name":"object detection","score":0.7369999885559082},{"id":"https://openalex.org/keywords/semantic-segmentation","display_name":"semantic segmentation","score":1.0},{"id":"https://openalex.org/keywords/autonomous-driving-perception","display_name":"autonomous driving perception","score":0.7860000133514404},{"id":"https://openalex.org/keywords/lightweight-deep-learning-models","display_name":"lightweight deep learning","score":0.6840000152587891},{"id":"https://openalex.org/keywords/edge-deployment","display_name":"edge deployment","score":0.9369999766349792},{"id":"https://openalex.org/keywords/traffic-scenes","display_name":"traffic scenes","score":0.5490000247955322},{"id":"https://openalex.org/keywords/atrous-spatial-pyramid-pooling","display_name":"atrous spatial pyramid pooling","score":0.9789999723434448},{"id":"https://openalex.org/keywords/focal-loss","display_name":"focal loss","score":0.8080000281333923},{"id":"https://openalex.org/keywords/nighttime-driving","display_name":"night driving","score":0.5400000214576721}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7894999980926514},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6937000155448914},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6826000213623047},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6277999877929688},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.607200026512146},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.5698999762535095},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5462999939918518},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5109999775886536},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.46230000257492065},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4535999894142151},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.4352000057697296},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.4207000136375427},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.3982999920845032},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.39820000529289246},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3885999917984009},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.38839998841285706},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.36419999599456787},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3628999888896942},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.35370001196861267},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.32120001316070557},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.3197999894618988},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30000001192092896},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C2780186347","wikidata":"https://www.wikidata.org/wiki/Q11414","display_name":"Subnetwork","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.28209999203681946},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.2800000011920929},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.273499995470047},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.266400009393692},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.2583000063896179}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s44163-026-02588-z","doi":"https://doi.org/10.1007/s44163-026-02588-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-02588-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-026-02588-z.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1","oarl"],"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:4855b47777c544da804a4d76cf556147","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/4855b47777c544da804a4d76cf556147","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,"listed_in":[],"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":"Discover Artificial Intelligence, Vol 6, Iss 1 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s44163-026-02588-z","doi":"https://doi.org/10.1007/s44163-026-02588-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-02588-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-026-02588-z.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1","oarl"],"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4943999946117401,"display_name":"Industry, innovation and infrastructure"},{"id":"https://metadata.un.org/sdg/11","score":0.47999998927116394,"display_name":"Sustainable cities and communities"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225460359.pdf","grobid_xml":"https://content.openalex.org/works/W7225460359.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W3196904463","https://openalex.org/W4205087957","https://openalex.org/W4205373550","https://openalex.org/W4205694850","https://openalex.org/W4294811323","https://openalex.org/W4296211836","https://openalex.org/W4311166797","https://openalex.org/W4316021899","https://openalex.org/W4375798315","https://openalex.org/W4376277293","https://openalex.org/W4385810785","https://openalex.org/W4387442092","https://openalex.org/W4401804758","https://openalex.org/W4401878967","https://openalex.org/W4402435496","https://openalex.org/W4406258841","https://openalex.org/W4407233086","https://openalex.org/W4408710134","https://openalex.org/W4410760901","https://openalex.org/W4411654803","https://openalex.org/W4417508459"],"related_works":["https://openalex.org/W4412508289","https://openalex.org/W7136072265","https://openalex.org/W4410144726","https://openalex.org/W7161113444","https://openalex.org/W2792472361","https://openalex.org/W4413376418","https://openalex.org/W7171425695","https://openalex.org/W7166833505","https://openalex.org/W4391097085","https://openalex.org/W4383820612"],"abstract_inverted_index":{"Autonomous":[0],"driving":[1],"perception":[2],"requires":[3],"simultaneous":[4],"object":[5],"detection":[6],"and":[7,18,32,78,89,99,112,116,136,140,143],"semantic":[8],"segmentation":[9],"under":[10],"complex":[11,185],"traffic":[12,186],"conditions":[13],"while":[14,121,159],"satisfying":[15],"the":[16,123,133,145,151],"memory":[17,163],"latency":[19],"constraints":[20],"of":[21,103],"edge":[22,182],"platforms.":[23],"To":[24],"address":[25],"this":[26],"requirement,":[27],"a":[28],"Lightweight":[29],"Collaborative":[30],"Detection":[31],"Segmentation":[33],"Network":[34],"(LCS-Net)":[35],"is":[36,165],"proposed.":[37],"The":[38],"network":[39],"decouples":[40],"spatial":[41,59,66],"representation":[42],"from":[43],"channel":[44],"mapping":[45],"through":[46,74],"deeply":[47],"cascaded":[48],"lightweight":[49],"convolutions.":[50],"It":[51],"integrates":[52],"multi-scale":[53],"context":[54],"using":[55],"an":[56,100,176],"improved":[57],"atrous":[58],"pyramid":[60],"pooling":[61],"module":[62],"alongside":[63],"bidirectional":[64],"gated":[65],"attention.":[67],"Furthermore,":[68],"it":[69,109,131],"balances":[70],"heterogeneous":[71],"task":[72,76],"convergence":[73],"dynamic":[75,79],"weighting":[77],"feature-pixel":[80],"focal":[81],"loss.":[82],"On":[83,150],"Cityscapes,":[84],"LCS-Net":[85,155,174],"achieves":[86],"82.53%":[87],"mAP@0.5":[88,111],"76.14%":[90],"mIoU":[91,113],"with":[92,107,129],"4.19":[93],"M":[94],"parameters,":[95],"14.71":[96],"G":[97],"FLOPs,":[98],"inference":[101,124,146],"speed":[102,125,147],"91.27":[104],"FPS.":[105],"Compared":[106,128],"YOLOP,":[108],"improves":[110],"by":[114,126,138,148],"6.12":[115],"5.96":[117],"percentage":[118],"points,":[119],"respectively,":[120,142],"increasing":[122],"34.56%.":[127],"YOLOPv2,":[130],"reduces":[132],"parameter":[134],"count":[135],"FLOPs":[137],"66.02%":[139],"64.36%,":[141],"increases":[144],"11.73%.":[149],"BDD100K":[152],"nighttime":[153],"subset,":[154],"maintains":[156],"72.19%":[157],"mAP@0.5,":[158],"its":[160],"peak":[161],"GPU":[162],"consumption":[164],"limited":[166],"to":[167],"1.6":[168],"GB.":[169],"These":[170],"results":[171],"indicate":[172],"that":[173],"provides":[175],"effective":[177],"accuracy\u2013efficiency":[178],"trade-off":[179],"for":[180],"resource-constrained":[181],"deployment":[183],"in":[184],"scenes.":[187]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-08T00:00:00"}