{"id":"https://openalex.org/W3009318725","doi":"https://doi.org/10.1109/access.2020.2978912","title":"Efficient Foreign Object Detection Between PSDs and Metro Doors via Deep Neural Networks","display_name":"Efficient Foreign Object Detection Between PSDs and Metro Doors via Deep Neural Networks","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3009318725","doi":"https://doi.org/10.1109/access.2020.2978912","mag":"3009318725"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.2978912","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2978912","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09026893.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/ielx7/6287639/8948470/09026893.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013271959","display_name":"Yuan Dai","orcid":"https://orcid.org/0000-0002-0408-5529"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Dai","raw_affiliation_strings":["School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-0408-5529","affiliations":[{"raw_affiliation_string":"School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043154142","display_name":"Weiming Liu","orcid":"https://orcid.org/0000-0003-1819-9153"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiming Liu","raw_affiliation_strings":["School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-1819-9153","affiliations":[{"raw_affiliation_string":"School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101574415","display_name":"Haiyu Li","orcid":"https://orcid.org/0000-0002-2097-1061"},"institutions":[{"id":"https://openalex.org/I4210145820","display_name":"Guangzhou Metro Group (China)","ror":"https://ror.org/04gfyef21","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210145820"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiyu Li","raw_affiliation_strings":["Guangzhou Metro Group Company, Ltd., Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-2097-1061","affiliations":[{"raw_affiliation_string":"Guangzhou Metro Group Company, Ltd., Guangzhou, China","institution_ids":["https://openalex.org/I4210145820"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100748127","display_name":"Lan Liu","orcid":"https://orcid.org/0000-0002-4429-7753"},"institutions":[{"id":"https://openalex.org/I4210145820","display_name":"Guangzhou Metro Group (China)","ror":"https://ror.org/04gfyef21","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210145820"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lan Liu","raw_affiliation_strings":["Guangzhou Metro Group Company, Ltd., Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-4429-7753","affiliations":[{"raw_affiliation_string":"Guangzhou Metro Group Company, Ltd., Guangzhou, China","institution_ids":["https://openalex.org/I4210145820"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.8208,"has_fulltext":true,"cited_by_count":29,"citation_normalized_percentile":{"value":0.8741575,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"8","issue":null,"first_page":"46723","last_page":"46734"},"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.996399998664856,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9919000267982483,"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/doors","display_name":"Doors","score":0.9419935941696167},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8151400089263916},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.8102967739105225},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7244006395339966},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7137744426727295},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5444546341896057},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5086930394172668},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.49371132254600525},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4792782962322235},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.46828028559684753},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4180009663105011},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.28755971789360046},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.23422804474830627},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1648038625717163},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08387482166290283}],"concepts":[{"id":"https://openalex.org/C125209513","wikidata":"https://www.wikidata.org/wiki/Q4037520","display_name":"Doors","level":2,"score":0.9419935941696167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8151400089263916},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.8102967739105225},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7244006395339966},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7137744426727295},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5444546341896057},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5086930394172668},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.49371132254600525},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4792782962322235},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.46828028559684753},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4180009663105011},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28755971789360046},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.23422804474830627},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1648038625717163},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08387482166290283},{"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/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.2978912","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2978912","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09026893.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:6b1f12bf40fb4240bf56ae07e6f3225d","is_oa":false,"landing_page_url":"https://doaj.org/article/6b1f12bf40fb4240bf56ae07e6f3225d","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 46723-46734 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.2978912","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2978912","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09026893.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":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3009318725.pdf","grobid_xml":"https://content.openalex.org/works/W3009318725.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1861492603","https://openalex.org/W2031489346","https://openalex.org/W2097117768","https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2120419212","https://openalex.org/W2127218421","https://openalex.org/W2161969291","https://openalex.org/W2194775991","https://openalex.org/W2307770531","https://openalex.org/W2348884780","https://openalex.org/W2570343428","https://openalex.org/W2739980858","https://openalex.org/W2944165510","https://openalex.org/W2953106684","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963323244","https://openalex.org/W2963351448","https://openalex.org/W2981958729","https://openalex.org/W2982770724","https://openalex.org/W3012573144","https://openalex.org/W3106250896","https://openalex.org/W4293584584","https://openalex.org/W6620707391","https://openalex.org/W6637373629","https://openalex.org/W6638444622","https://openalex.org/W6638667902","https://openalex.org/W6639102338","https://openalex.org/W6678914141","https://openalex.org/W6697925102","https://openalex.org/W6750227808","https://openalex.org/W6753494528","https://openalex.org/W6760424586","https://openalex.org/W6762082143","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2095705906","https://openalex.org/W2975200075","https://openalex.org/W4312857205","https://openalex.org/W2732308154","https://openalex.org/W1988485990","https://openalex.org/W3177406559","https://openalex.org/W2334336442","https://openalex.org/W2970686063","https://openalex.org/W2911294201","https://openalex.org/W3009318725"],"abstract_inverted_index":{"Platform":[0],"Screen":[1],"Doors":[2],"(PSDs)":[3],"have":[4,72,196],"been":[5],"widely":[6],"used":[7],"in":[8,76],"modern":[9],"Asian":[10],"and":[11,33,50,63,102,177],"European":[12],"metro":[13,34,51,103],"systems":[14],"due":[15],"to":[16,40,92,116,120,226,240],"the":[17,44,94,106,113,122,157,180,186,204,211,221,235],"advantages":[18],"of":[19,96,108,142,159,206,213],"safety,":[20],"comfort":[21],"for":[22,139],"passengers.":[23],"Unfortunately,":[24],"someone":[25],"or":[26],"something":[27],"will":[28],"be":[29],"caught":[30],"by":[31],"PSDs":[32,49,101],"doors":[35,52],"occasionally,":[36],"which":[37,200],"may":[38],"lead":[39],"serious":[41],"accidents.":[42],"Therefore,":[43],"foreign":[45,97,143,187,214],"object":[46,78,98,162,188],"detection":[47,79,99,163,189,207,223,237],"between":[48,100],"is":[53,60,112,152],"a":[54,61,127],"burning":[55],"problem.":[56,123],"Moreover,":[57],"this":[58,86,111],"problem":[59,95],"challenging":[62],"still":[64],"largely":[65],"under-explored":[66],"topic.":[67],"In":[68],"recent":[69],"years,":[70],"we":[71,155],"seen":[73],"significant":[74],"improvements":[75],"generic":[77],"built":[80],"on":[81,179,192],"deep":[82,89,118,193],"learning":[83,90,119],"techniques.":[84],"Accordingly,":[85],"paper":[87],"adopts":[88],"technologies":[91],"address":[93],"doors.":[104],"To":[105,124],"best":[107,236],"our":[109],"knowledge,":[110],"first":[114],"attempt":[115],"use":[117],"solve":[121],"realize":[125],"this,":[126],"dataset":[128],"including":[129],"984":[130],"real-world":[131],"images":[132],"(with":[133],"600":[134],"\u00d7":[135],"480":[136],"pixels)":[137],"labeled":[138],"six":[140],"types":[141],"objects":[144],"(bag,":[145],"bottle,":[146],"person,":[147],"plastic":[148],"bag,":[149],"umbrella,":[150],"other)":[151],"developed.":[153],"Then,":[154],"compared":[156],"performance":[158],"some":[160],"state-of-the-art":[161],"algorithms":[164,190],"(such":[165],"as":[166],"You":[167],"Only":[168],"Look":[169],"Once":[170],"-YOLOv3,":[171],"Single":[172],"Shot":[173],"MultiBox":[174],"Detector":[175],"-SSD,":[176],"CenterNet)":[178],"dataset.":[181],"Experimental":[182],"results":[183],"demonstrate":[184],"that":[185],"based":[191],"neural":[194],"networks":[195],"achieved":[197],"excellent":[198],"results,":[199,238],"not":[201],"only":[202],"improves":[203],"accuracy":[205],"but":[208],"also":[209],"give":[210],"categories":[212],"objects.":[215],"YOLOv3":[216],"-":[217],"tiny":[218],"can":[219,233],"achieve":[220,234],"fastest":[222],"speed,":[224],"up":[225,239],"200":[227],"Frame":[228],"Per":[229],"Second":[230],"(FPS);":[231],"CenterNet":[232],"99.7%":[241],"mean":[242],"Average":[243],"Precision":[244],"(mAP).":[245]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-13T07:31:44.756512","created_date":"2025-10-10T00:00:00"}
