{"id":"https://openalex.org/W4415003003","doi":"https://doi.org/10.1109/tits.2025.3615568","title":"SL-Seg: A CNN-Transformer Fusion Network for Road Surface and Lane Segmentation in Complex Scenarios","display_name":"SL-Seg: A CNN-Transformer Fusion Network for Road Surface and Lane Segmentation in Complex Scenarios","publication_year":2025,"publication_date":"2025-10-09","ids":{"openalex":"https://openalex.org/W4415003003","doi":"https://doi.org/10.1109/tits.2025.3615568"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2025.3615568","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3615568","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-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":null,"display_name":"Chenlin Meng","orcid":"https://orcid.org/0009-0001-9661-0131"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenlin Meng","raw_affiliation_strings":["Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, China"],"raw_orcid":"https://orcid.org/0009-0001-9661-0131","affiliations":[{"raw_affiliation_string":"Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xin Wang","orcid":"https://orcid.org/0000-0002-0054-7749"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0002-0054-7749","affiliations":[{"raw_affiliation_string":"Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111285707","display_name":"Qinhao Tu","orcid":null},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinhao Tu","raw_affiliation_strings":["School of Software, Northwestern Polytechnical University, Xi’an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068625837","display_name":"Zhaoyong Mao","orcid":"https://orcid.org/0000-0001-5746-6133"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaoyong Mao","raw_affiliation_strings":["Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0001-5746-6133","affiliations":[{"raw_affiliation_string":"Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081646661","display_name":"Junge Shen","orcid":"https://orcid.org/0000-0002-6563-9206"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junge Shen","raw_affiliation_strings":["Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0002-6563-9206","affiliations":[{"raw_affiliation_string":"Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":1.1068,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.76499669,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"26","issue":"12","first_page":"22761","last_page":"22776"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9889000058174133,"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"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9889000058174133,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9789000153541565,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9775000214576721,"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/segmentation","display_name":"Segmentation","score":0.7730000019073486},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.43209999799728394},{"id":"https://openalex.org/keywords/road-surface","display_name":"Road surface","score":0.4309999942779541},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.428600013256073},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4271000027656555},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41499999165534973},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.40849998593330383},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.40389999747276306},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4027999937534332}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7730000019073486},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6388000249862671},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6018999814987183},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5577999949455261},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.43209999799728394},{"id":"https://openalex.org/C2780042925","wikidata":"https://www.wikidata.org/wiki/Q1049667","display_name":"Road surface","level":2,"score":0.4309999942779541},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.428600013256073},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4271000027656555},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41499999165534973},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.40849998593330383},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.40389999747276306},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4027999937534332},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.37540000677108765},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3750999867916107},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.35249999165534973},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.35120001435279846},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3458000123500824},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.32659998536109924},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.325300008058548},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.3116999864578247},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.28999999165534973},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2025.3615568","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3615568","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4478686145","display_name":null,"funder_award_id":"U24B20115","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G737538665","display_name":null,"funder_award_id":"2023GXLH-100","funder_id":"https://openalex.org/F4320329794","funder_display_name":"Social Science Foundation of Shaanxi Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320329794","display_name":"Social Science Foundation of Shaanxi Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1983990893","https://openalex.org/W2074100968","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2344934580","https://openalex.org/W2513332682","https://openalex.org/W2531409750","https://openalex.org/W2560023338","https://openalex.org/W2787091153","https://openalex.org/W2886934227","https://openalex.org/W2910628332","https://openalex.org/W2963163009","https://openalex.org/W2963351448","https://openalex.org/W2963418739","https://openalex.org/W2963604034","https://openalex.org/W2963815618","https://openalex.org/W2963840672","https://openalex.org/W2971198903","https://openalex.org/W2982083293","https://openalex.org/W2996106355","https://openalex.org/W3008115128","https://openalex.org/W3034175346","https://openalex.org/W3034552520","https://openalex.org/W3035564946","https://openalex.org/W3084892460","https://openalex.org/W3086544482","https://openalex.org/W3131500599","https://openalex.org/W3132455321","https://openalex.org/W3137481847","https://openalex.org/W3138516171","https://openalex.org/W3190492058","https://openalex.org/W3194912724","https://openalex.org/W3196904463","https://openalex.org/W3197017665","https://openalex.org/W4205977910","https://openalex.org/W4214520160","https://openalex.org/W4280579601","https://openalex.org/W4293652213","https://openalex.org/W4294811323","https://openalex.org/W4296229087","https://openalex.org/W4298088457","https://openalex.org/W4312785900","https://openalex.org/W4315607730","https://openalex.org/W4372347372","https://openalex.org/W4376562192","https://openalex.org/W4385413647","https://openalex.org/W4385624401","https://openalex.org/W4386075553","https://openalex.org/W4388206698","https://openalex.org/W4388936997","https://openalex.org/W4389301963","https://openalex.org/W4391128608","https://openalex.org/W4391935983","https://openalex.org/W4395447492","https://openalex.org/W4396917969","https://openalex.org/W4401070841","https://openalex.org/W4403784907"],"related_works":[],"abstract_inverted_index":{"Road":[0],"image":[1],"segmentation":[2,15,56,73,97],"plays":[3],"a":[4,77,81,113,128,162],"pivotal":[5],"role":[6],"in":[7,33,58,179],"traffic":[8,24,187],"video":[9],"surveillance":[10,37,60,188],"for":[11,22,161,166,174],"environmental":[12],"perception.":[13],"Precise":[14],"of":[16,123,186],"roads":[17],"and":[18,26,47,111,169],"lanes":[19],"is":[20,92,133,146,177],"essential":[21],"effective":[23],"monitoring":[25],"management.":[27],"However,":[28],"unlike":[29],"the":[30,36,55,90,121,149,159,184,197,202],"perspective":[31,38],"encountered":[32],"autonomous":[34],"driving,":[35],"poses":[39],"unique":[40],"challenges":[41],"due":[42],"to":[43,49,94,119,135,182],"its":[44],"wider":[45],"scope":[46],"susceptibility":[48],"complex":[50,175],"environments.":[51],"This":[52,106],"complexity":[53],"makes":[54],"task":[57],"road":[59,167],"videos":[61],"particularly":[62],"demanding.":[63],"To":[64],"overcome":[65],"these":[66],"challenges,":[67],"we":[68],"introduce":[69],"an":[70,140],"end-to-end":[71],"semantic":[72],"network":[74],"that":[75,193],"leverages":[76],"CNN-Transformer":[78],"architecture.":[79],"Firstly,":[80],"spatial":[82],"pyramid":[83],"attention-style":[84],"convolution":[85],"(SP-AttnConv)":[86],"module,":[87],"built":[88,178],"upon":[89],"Transformer":[91],"introduced,":[93],"ensure":[95],"accurate":[96],"across":[98],"long":[99],"distances":[100],"while":[101],"preserving":[102],"fine":[103],"boundary":[104],"information.":[105],"module":[107,132,145],"enhances":[108],"local":[109],"information":[110],"fosters":[112],"\u201cglobal-local\u201d":[114],"feature":[115],"fusion":[116],"framework.":[117],"Secondly,":[118],"tackle":[120],"issue":[122],"scale":[124],"imbalance":[125],"during":[126],"segmentation,":[127,171],"lightweight":[129],"multi-scale":[130,137],"(LMS)":[131],"introduced":[134],"capture":[136],"feature.":[138],"Additionally,":[139],"occlusion":[141],"relief":[142],"branch":[143],"(ORB)":[144],"integrated":[147],"into":[148],"decoder,":[150],"specifically":[151],"addressing":[152],"occlusions":[153],"caused":[154],"by":[155],"irrelevant":[156],"objects.":[157],"Recognizing":[158],"need":[160],"dedicated":[163],"benchmark":[164],"dataset":[165],"surface":[168],"lane":[170],"surface-lane":[172],"(SL)":[173],"scenarios":[176],"our":[180,194],"paper":[181],"promote":[183],"development":[185],"system.":[189],"Comparative":[190],"experiments":[191],"demonstrate":[192],"method":[195],"achieves":[196],"best":[198],"overall":[199],"performance":[200],"on":[201],"SL":[203],"dataset.":[204]},"counts_by_year":[{"year":2026,"cited_by_count":4}],"updated_date":"2026-08-22T07:34:49.880490","created_date":"2025-10-10T00:00:00"}