{"id":"https://openalex.org/W3129264977","doi":"https://doi.org/10.1109/vtc2020-fall49728.2020.9348862","title":"DaliNet: Lighter, deeper and larger field of vision","display_name":"DaliNet: Lighter, deeper and larger field of vision","publication_year":2020,"publication_date":"2020-11-01","ids":{"openalex":"https://openalex.org/W3129264977","doi":"https://doi.org/10.1109/vtc2020-fall49728.2020.9348862","mag":"3129264977"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2020-fall49728.2020.9348862","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2020-fall49728.2020.9348862","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall)","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/A5103050216","display_name":"Hao Fu","orcid":"https://orcid.org/0009-0000-8086-7779"},"institutions":[{"id":"https://openalex.org/I47689461","display_name":"Northeast Forestry University","ror":"https://ror.org/02yxnh564","country_code":"CN","type":"education","lineage":["https://openalex.org/I47689461"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hao Fu","raw_affiliation_strings":["School of Information and computer engineering, Northeast Forestry University"],"affiliations":[{"raw_affiliation_string":"School of Information and computer engineering, Northeast Forestry University","institution_ids":["https://openalex.org/I47689461"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100677212","display_name":"Yuyan Wang","orcid":"https://orcid.org/0000-0001-5585-3789"},"institutions":[{"id":"https://openalex.org/I47689461","display_name":"Northeast Forestry University","ror":"https://ror.org/02yxnh564","country_code":"CN","type":"education","lineage":["https://openalex.org/I47689461"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuyan Wang","raw_affiliation_strings":["School of Information and computer engineering, Northeast Forestry University"],"affiliations":[{"raw_affiliation_string":"School of Information and computer engineering, Northeast Forestry University","institution_ids":["https://openalex.org/I47689461"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100436443","display_name":"Zhipeng Yang","orcid":"https://orcid.org/0000-0001-9378-0613"},"institutions":[{"id":"https://openalex.org/I47689461","display_name":"Northeast Forestry University","ror":"https://ror.org/02yxnh564","country_code":"CN","type":"education","lineage":["https://openalex.org/I47689461"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhipeng Yang","raw_affiliation_strings":["School of Information and computer engineering, Northeast Forestry University"],"affiliations":[{"raw_affiliation_string":"School of Information and computer engineering, Northeast Forestry University","institution_ids":["https://openalex.org/I47689461"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005360677","display_name":"Jiazheng Fu","orcid":null},"institutions":[{"id":"https://openalex.org/I47689461","display_name":"Northeast Forestry University","ror":"https://ror.org/02yxnh564","country_code":"CN","type":"education","lineage":["https://openalex.org/I47689461"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiazheng Fu","raw_affiliation_strings":["School of Information and computer engineering, Northeast Forestry University"],"affiliations":[{"raw_affiliation_string":"School of Information and computer engineering, Northeast Forestry University","institution_ids":["https://openalex.org/I47689461"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087555148","display_name":"Cheng Feng","orcid":"https://orcid.org/0000-0001-5941-1240"},"institutions":[{"id":"https://openalex.org/I47689461","display_name":"Northeast Forestry University","ror":"https://ror.org/02yxnh564","country_code":"CN","type":"education","lineage":["https://openalex.org/I47689461"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Feng","raw_affiliation_strings":["School of Information and computer engineering, Northeast Forestry University"],"affiliations":[{"raw_affiliation_string":"School of Information and computer engineering, Northeast Forestry University","institution_ids":["https://openalex.org/I47689461"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100698922","display_name":"Tian Xia","orcid":"https://orcid.org/0000-0001-9887-4993"},"institutions":[{"id":"https://openalex.org/I100188998","display_name":"Harbin University of Science and Technology","ror":"https://ror.org/04e6y1282","country_code":"CN","type":"education","lineage":["https://openalex.org/I100188998"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tian Xia","raw_affiliation_strings":["School of Automation, Harbin University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"School of Automation, Harbin University of Science and Technology","institution_ids":["https://openalex.org/I100188998"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5103050216"],"corresponding_institution_ids":["https://openalex.org/I47689461"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17713585,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"15","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9897000193595886,"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/computer-science","display_name":"Computer science","score":0.8079193830490112},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.7841024398803711},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7108341455459595},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6251107454299927},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5967484712600708},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.5398539304733276},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5339810848236084},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.511662483215332},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5077110528945923},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5032204985618591},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4920746684074402},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4812578856945038},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4653032422065735},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4335111379623413},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4128872752189636},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3986700177192688},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3483878970146179},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3465225100517273},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.0929899513721466}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8079193830490112},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.7841024398803711},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7108341455459595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6251107454299927},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5967484712600708},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.5398539304733276},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5339810848236084},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.511662483215332},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5077110528945923},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5032204985618591},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4920746684074402},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4812578856945038},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4653032422065735},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4335111379623413},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4128872752189636},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3986700177192688},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3483878970146179},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3465225100517273},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0929899513721466},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2020-fall49728.2020.9348862","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2020-fall49728.2020.9348862","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1901129140","https://openalex.org/W1910657905","https://openalex.org/W2095705004","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2412782625","https://openalex.org/W2531409750","https://openalex.org/W2560023338","https://openalex.org/W2562137921","https://openalex.org/W2612445135","https://openalex.org/W2783000019","https://openalex.org/W2787091153","https://openalex.org/W2963351448","https://openalex.org/W2963881378","https://openalex.org/W2964309882","https://openalex.org/W2996290406","https://openalex.org/W4293584584","https://openalex.org/W4297775537","https://openalex.org/W4299978048","https://openalex.org/W6637373629","https://openalex.org/W6674330103","https://openalex.org/W6684191040","https://openalex.org/W6687483927","https://openalex.org/W6730565506","https://openalex.org/W6737664043","https://openalex.org/W6748481559","https://openalex.org/W6750227808"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2127970246","https://openalex.org/W2885125400","https://openalex.org/W1989889224","https://openalex.org/W4382618745","https://openalex.org/W1973775000","https://openalex.org/W2748922771","https://openalex.org/W1987128138","https://openalex.org/W2743976221"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"with":[3],"the":[4,11,17,45,63,71,75,118,133,153],"in-depth":[5],"development":[6],"of":[7,13,19,52,58,65,74,86,129,135],"semantic":[8,22,37],"segmentation":[9,23,38],"in":[10,30,42,117],"field":[12,85],"artificial":[14],"intelligence":[15],"and":[16,94,113,131,138,152],"emergence":[18],"deep":[20],"learning,":[21],"is":[24,48],"playing":[25],"an":[26,100],"increasingly":[27],"important":[28],"role":[29],"driverless":[31],"vehicles.":[32],"At":[33],"present,":[34],"although":[35],"some":[36],"models":[39],"perform":[40],"well":[41],"road":[43,142],"recognition,":[44],"training":[46],"process":[47],"very":[49],"complicated.":[50],"Most":[51],"them":[53],"require":[54],"a":[55,83,95,126],"huge":[56],"number":[57],"data":[59],"sets":[60],"to":[61],"achieve":[62],"goal":[64],"high":[66,136],"accuracy.":[67],"This":[68],"undoubtedly":[69],"limits":[70],"applied":[72],"conditions":[73],"model.":[76,120],"So":[77],"we":[78],"propose":[79],"DaliNet,":[80],"which":[81],"has":[82],"wider":[84],"view":[87],"for":[88,141],"image":[89],"feature":[90],"extraction,":[91],"fewer":[92],"parameters,":[93],"deeper":[96],"network.":[97],"We":[98,144],"introduced":[99],"improved":[101],"3x3":[102],"convolutional":[103],"neural":[104],"network,":[105,107],"residual":[106],"parallel":[108],"hole":[109],"convolution,":[110],"bottleneck":[111],"structure":[112,116],"inverted":[114],"pyramid":[115],"DaliNet":[119,121],"can":[122],"be":[123],"trained":[124],"on":[125,147],"small":[127],"dataset":[128],"Kitti":[130],"showed":[132],"characteristics":[134],"accuracy":[137],"low":[139],"latency":[140],"recognition.":[143],"conducted":[145],"experiments":[146],"Google":[148],"colab":[149],"(Telas":[150],"T4)":[151],"average":[154],"processing":[155],"time":[156],"was":[157],"only":[158],"0.18":[159],"seconds.":[160]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
