{"id":"https://openalex.org/W4366259146","doi":"https://doi.org/10.1109/ccwc57344.2023.10099233","title":"Road Curb Detection Based on a Deep Learning Framework","display_name":"Road Curb Detection Based on a Deep Learning Framework","publication_year":2023,"publication_date":"2023-03-08","ids":{"openalex":"https://openalex.org/W4366259146","doi":"https://doi.org/10.1109/ccwc57344.2023.10099233"},"language":"en","primary_location":{"id":"doi:10.1109/ccwc57344.2023.10099233","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ccwc57344.2023.10099233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC)","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/A5101966891","display_name":"Min Zou","orcid":"https://orcid.org/0000-0001-9761-0793"},"institutions":[{"id":"https://openalex.org/I203765153","display_name":"Akita University","ror":"https://ror.org/03hv1ad10","country_code":"JP","type":"education","lineage":["https://openalex.org/I203765153"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Min Zou","raw_affiliation_strings":["Graduate School of Engineering Science Akita University,Akita City,Japan","Graduate School of Engineering Science Akita University, Akita City, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering Science Akita University,Akita City,Japan","institution_ids":["https://openalex.org/I203765153"]},{"raw_affiliation_string":"Graduate School of Engineering Science Akita University, Akita City, Japan","institution_ids":["https://openalex.org/I203765153"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030252329","display_name":"Yoichi Kageyama","orcid":"https://orcid.org/0000-0001-9958-1228"},"institutions":[{"id":"https://openalex.org/I203765153","display_name":"Akita University","ror":"https://ror.org/03hv1ad10","country_code":"JP","type":"education","lineage":["https://openalex.org/I203765153"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoichi Kageyama","raw_affiliation_strings":["Graduate School of Engineering Science Akita University,Akita City,Japan","Graduate School of Engineering Science Akita University, Akita City, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering Science Akita University,Akita City,Japan","institution_ids":["https://openalex.org/I203765153"]},{"raw_affiliation_string":"Graduate School of Engineering Science Akita University, Akita City, Japan","institution_ids":["https://openalex.org/I203765153"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2836,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.51713535,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"28","issue":null,"first_page":"0259","last_page":"0262"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9994000196456909,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9980000257492065,"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":0.9975000023841858,"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/softmax-function","display_name":"Softmax function","score":0.9764016270637512},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8096008896827698},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.7521811127662659},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7344785928726196},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.7048349380493164},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6971602439880371},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6173666715621948},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5722683072090149},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5556975603103638},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.47496339678764343},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.4694991111755371},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.45456674695014954},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4240506887435913},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.37464144825935364},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2548618018627167}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.9764016270637512},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8096008896827698},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.7521811127662659},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7344785928726196},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.7048349380493164},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6971602439880371},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6173666715621948},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5722683072090149},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5556975603103638},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.47496339678764343},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.4694991111755371},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.45456674695014954},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4240506887435913},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.37464144825935364},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2548618018627167},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccwc57344.2023.10099233","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ccwc57344.2023.10099233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.550000011920929,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2159132531","https://openalex.org/W2194775991","https://openalex.org/W2803692342","https://openalex.org/W3003936655","https://openalex.org/W3096870416","https://openalex.org/W3120361977","https://openalex.org/W3132952762","https://openalex.org/W4282915433","https://openalex.org/W6620707391"],"related_works":["https://openalex.org/W3107204728","https://openalex.org/W2962734882","https://openalex.org/W2185726654","https://openalex.org/W3134502938","https://openalex.org/W1034159276","https://openalex.org/W2748667022","https://openalex.org/W4229443568","https://openalex.org/W3211770882","https://openalex.org/W2010629964","https://openalex.org/W2983135586"],"abstract_inverted_index":{"Detecting":[0],"curbs":[1,40],"efficiently":[2],"and":[3,22,28,73,82,108,150],"at":[4],"a":[5,26,42,66],"low":[6],"cost":[7],"is":[8,25,133],"essential":[9],"for":[10,38,126,148,161],"autonomous":[11],"driving.":[12],"Most":[13],"of":[14,113,121,144,168,188],"the":[15,49,57,61,86,119,122,136,142,153,157,165,169,172,185,189],"existing":[16],"methods":[17],"rely":[18],"on":[19],"light":[20],"detection":[21,154,173],"ranging,":[23],"which":[24,95],"complicated":[27],"costly":[29],"solution.":[30],"Therefore,":[31],"this":[32],"work":[33],"propose":[34],"an":[35],"efficient":[36],"method":[37,47],"detecting":[39],"in":[41,60,85,135],"single":[43],"road":[44],"image.":[45,63],"This":[46],"utilizes":[48],"convolutional":[50],"neural":[51],"network":[52,124,131,181],"(CNN)":[53],"framework":[54],"to":[55,117,140],"localize":[56],"curb":[58,72,127],"parts":[59],"captured":[62],"We":[64],"built":[65],"feature-extraction":[67],"CNN":[68,180],"that":[69,177],"can":[70,183],"classify":[71],"no-curb":[74],"regions.":[75],"The":[76,111,129],"relu,":[77,97,103],"pooling,":[78],"fully":[79,101,105],"connected,":[80,102,106],"softmax,":[81,107],"classification":[83,109,149,186],"layers":[84],"pretrained":[87],"ResNet18":[88],"are":[89,96],"replaced":[90],"with":[91],"ten":[92],"new":[93],"layers,":[94,171],"convolution2d,":[98],"batchnormalization,":[99],"dropout,":[100],"maxpooling2d,":[104],"layers.":[110],"comparison":[112],"several":[114],"evaluation":[115],"metrics":[116],"prove":[118],"effectiveness":[120],"customized":[123,130],"structure":[125,132,182],"classification.":[128],"assembled":[134],"Faster":[137],"R-CNN":[138],"detector":[139],"improve":[141,184],"performance":[143,187],"feature":[145,162,190],"extraction":[146,163,191],"both":[147],"detection.":[151],"In":[152],"stage,":[155],"although":[156],"layer":[158,167],"we":[159],"used":[160],"was":[164],"previous":[166],"added":[170],"experimental":[174],"results":[175],"demonstrate":[176],"our":[178],"custom":[179],"layer.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
