{"id":"https://openalex.org/W3042793842","doi":"https://doi.org/10.1109/access.2020.3009782","title":"Road Scene Segmentation Based on Deep Learning","display_name":"Road Scene Segmentation Based on Deep Learning","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3042793842","doi":"https://doi.org/10.1109/access.2020.3009782","mag":"3042793842"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3009782","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3009782","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09142221.pdf","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://ieeexplore.ieee.org/ielx7/6287639/8948470/09142221.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045202460","display_name":"Ke Zheng","orcid":"https://orcid.org/0000-0003-2572-2635"},"institutions":[{"id":"https://openalex.org/I4210115515","display_name":"Nanyang Institute of Technology","ror":"https://ror.org/0203c2755","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210115515"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Zheng","raw_affiliation_strings":["School of Computer and Software, Nanyang Institute of Technology, Nanyang, China"],"raw_orcid":"https://orcid.org/0000-0003-2572-2635","affiliations":[{"raw_affiliation_string":"School of Computer and Software, Nanyang Institute of Technology, Nanyang, China","institution_ids":["https://openalex.org/I4210115515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001586388","display_name":"Hasan A. H. Naji","orcid":"https://orcid.org/0000-0002-2035-0759"},"institutions":[{"id":"https://openalex.org/I4210115515","display_name":"Nanyang Institute of Technology","ror":"https://ror.org/0203c2755","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210115515"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hasan Abdullah Hasan Naji","raw_affiliation_strings":["School of Digital Media, Nanyang Institute of Technology, Nanyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Digital Media, Nanyang Institute of Technology, Nanyang, China","institution_ids":["https://openalex.org/I4210115515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210115515"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.2875,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.55442368,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"8","issue":null,"first_page":"140964","last_page":"140971"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9991000294685364,"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":0.9991000294685364,"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":0.996399998664856,"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":0.9864000082015991,"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":0.8222424983978271},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.668235719203949},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6337589621543884},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.586995005607605},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5575093626976013},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5490133166313171},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.5328230261802673},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4969828426837921},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.45569223165512085},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.44630002975463867},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43059250712394714}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8222424983978271},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.668235719203949},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6337589621543884},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.586995005607605},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5575093626976013},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5490133166313171},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.5328230261802673},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4969828426837921},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.45569223165512085},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.44630002975463867},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43059250712394714},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3009782","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3009782","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09142221.pdf","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:afe8d46581d8406ea463e3528581cf5e","is_oa":true,"landing_page_url":"https://doaj.org/article/afe8d46581d8406ea463e3528581cf5e","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 8, Pp 140964-140971 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3009782","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3009782","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09142221.pdf","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":[{"score":0.6499999761581421,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G2479333644","display_name":null,"funder_award_id":"162102210358","funder_id":"https://openalex.org/F4320327051","funder_display_name":"Science and Technology Department of Henan Province"}],"funders":[{"id":"https://openalex.org/F4320327051","display_name":"Science and Technology Department of Henan Province","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3042793842.pdf","grobid_xml":"https://content.openalex.org/works/W3042793842.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1903029394","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2116360511","https://openalex.org/W2157076480","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2302255633","https://openalex.org/W2311778361","https://openalex.org/W2599199509","https://openalex.org/W2611223806","https://openalex.org/W2788097838","https://openalex.org/W2895922401","https://openalex.org/W2948858413","https://openalex.org/W2964350391","https://openalex.org/W3209067545","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6694260854","https://openalex.org/W6698183232","https://openalex.org/W6803470823"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W2731899572","https://openalex.org/W4230611425","https://openalex.org/W2062195135","https://openalex.org/W4294635752","https://openalex.org/W4304166257","https://openalex.org/W4383066092","https://openalex.org/W3112772842","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Recently,":[0],"autonomous":[1],"driving":[2,13],"becomes":[3],"a":[4,42,48,58,72],"hot":[5],"topic":[6],"in":[7,24,85,119],"research":[8],"and":[9,27,47,83,106,112],"industry":[10],"area.":[11],"Autonomous":[12],"technology":[14],"needs":[15],"to":[16,78,116],"perceive":[17],"the":[18,25,33,80,86,89,96,102],"semantic":[19,34,49,59],"information":[20],"of":[21,36,52,62,88],"road":[22,53,63,104,120],"scenes":[23,38,54,64],"all-day":[26],"open":[28],"environment.":[29],"In":[30],"this":[31],"article,":[32],"recognition":[35,60],"traffic":[37],"is":[39,55,69,76,93],"studied":[40],"using":[41],"deep":[43],"learning":[44],"network":[45],"model,":[46,82],"representation":[50],"model":[51],"established.":[56],"Besides,":[57],"algorithm":[61],"based":[65],"on":[66],"image":[67],"data":[68,74],"proposed.":[70],"Finally,":[71],"self-built":[73],"set":[75],"used":[77,118],"train":[79],"proposed":[81,97],"verified":[84],"field":[87],"test":[90],"vehicle.":[91],"It":[92],"found":[94],"that":[95],"method":[98],"can":[99],"quickly":[100],"capture":[101],"perceptual":[103],"scene":[105,121],"over-performs":[107],"better":[108],"than":[109],"traditional":[110],"methods":[111],"demonstrated":[113],"great":[114],"potential":[115],"be":[117],"applications.":[122]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}