{"id":"https://openalex.org/W2921725755","doi":"https://doi.org/10.1109/tits.2019.2900385","title":"Defect Detection of Pantograph Slide Based on Deep Learning and Image Processing Technology","display_name":"Defect Detection of Pantograph Slide Based on Deep Learning and Image Processing Technology","publication_year":2019,"publication_date":"2019-03-15","ids":{"openalex":"https://openalex.org/W2921725755","doi":"https://doi.org/10.1109/tits.2019.2900385","mag":"2921725755"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2019.2900385","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2019.2900385","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":"https://openalex.org/A5051900939","display_name":"Xiukun Wei","orcid":"https://orcid.org/0000-0003-0341-966X"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiukun Wei","raw_affiliation_strings":["State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0341-966X","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101610080","display_name":"Siyang Jiang","orcid":"https://orcid.org/0000-0002-8527-5526"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyang Jiang","raw_affiliation_strings":["State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8527-5526","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100443878","display_name":"Yan Li","orcid":"https://orcid.org/0000-0002-4028-7507"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Li","raw_affiliation_strings":["Shengzhou Tongzhen Technology Company, Beijing, China","State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shengzhou Tongzhen Technology Company, Beijing, China","institution_ids":[]},{"raw_affiliation_string":"State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chenliang Li","orcid":"https://orcid.org/0000-0003-1787-572X"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenliang Li","raw_affiliation_strings":["State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1787-572X","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100680032","display_name":"Limin Jia","orcid":"https://orcid.org/0000-0003-2161-4637"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Limin Jia","raw_affiliation_strings":["State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2161-4637","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100637039","display_name":"Yongguang Li","orcid":"https://orcid.org/0000-0001-8417-3336"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongguang Li","raw_affiliation_strings":["Shengzhou Tongzhen Technology Company, Beijing, China","State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shengzhou Tongzhen Technology Company, Beijing, China","institution_ids":[]},{"raw_affiliation_string":"State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":6.3281,"has_fulltext":false,"cited_by_count":108,"citation_normalized_percentile":{"value":0.97719126,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"21","issue":"3","first_page":"947","last_page":"958"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12371","display_name":"Electrical Contact Performance and Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T12371","display_name":"Electrical Contact Performance and Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T10842","display_name":"Railway Engineering and Dynamics","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T12252","display_name":"Mechanical stress and fatigue analysis","score":0.9882000088691711,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/pantograph","display_name":"Pantograph","score":0.9857673645019531},{"id":"https://openalex.org/keywords/catenary","display_name":"Catenary","score":0.7599503993988037},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5577237010002136},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.546630859375},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5223768353462219},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4934886693954468},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.46371224522590637},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4451771378517151},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4391064941883087},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43647685647010803},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.29179394245147705},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.24763455986976624},{"id":"https://openalex.org/keywords/structural-engineering","display_name":"Structural engineering","score":0.22967100143432617}],"concepts":[{"id":"https://openalex.org/C20756127","wikidata":"https://www.wikidata.org/wiki/Q722757","display_name":"Pantograph","level":2,"score":0.9857673645019531},{"id":"https://openalex.org/C29279314","wikidata":"https://www.wikidata.org/wiki/Q214561","display_name":"Catenary","level":2,"score":0.7599503993988037},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5577237010002136},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.546630859375},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5223768353462219},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4934886693954468},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.46371224522590637},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4451771378517151},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4391064941883087},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43647685647010803},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.29179394245147705},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.24763455986976624},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.22967100143432617}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2019.2900385","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2019.2900385","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":[{"id":"https://metadata.un.org/sdg/9","score":0.4699999988079071,"display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G6496088512","display_name":null,"funder_award_id":"RCS2019ZT004","funder_id":"https://openalex.org/F4320323067","funder_display_name":"State Key Laboratory of Rail Traffic Control and Safety"}],"funders":[{"id":"https://openalex.org/F4320323067","display_name":"State Key Laboratory of Rail Traffic Control and Safety","ror":"https://ror.org/01yj56c84"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W810297366","https://openalex.org/W1533162639","https://openalex.org/W1686810756","https://openalex.org/W1999663599","https://openalex.org/W2012986458","https://openalex.org/W2016839094","https://openalex.org/W2043624558","https://openalex.org/W2057379803","https://openalex.org/W2062310164","https://openalex.org/W2063602960","https://openalex.org/W2102605133","https://openalex.org/W2108321183","https://openalex.org/W2112796928","https://openalex.org/W2148988705","https://openalex.org/W2163605009","https://openalex.org/W2356344889","https://openalex.org/W2378369135","https://openalex.org/W2535776261","https://openalex.org/W2555344168","https://openalex.org/W2782601073","https://openalex.org/W2785606247","https://openalex.org/W2980086557","https://openalex.org/W2995184771","https://openalex.org/W6684191040","https://openalex.org/W6728938042","https://openalex.org/W6748120745"],"related_works":["https://openalex.org/W2352865025","https://openalex.org/W2939045761","https://openalex.org/W2355211658","https://openalex.org/W2067167195","https://openalex.org/W2057969614","https://openalex.org/W2361809279","https://openalex.org/W2670755988","https://openalex.org/W2387550916","https://openalex.org/W2389297227","https://openalex.org/W2937048722"],"abstract_inverted_index":{"Pantograph":[0],"is":[1,106,134,156,187],"one":[2,40],"of":[3,22,54,69,76,85,113,129,140,205,228],"the":[4,18,20,23,33,43,52,55,63,67,77,109,114,119,122,130,141,159,165,173,197,202,206,214,219,225,246],"most":[5],"important":[6],"components":[7],"in":[8,83,189],"electrical":[9],"railway":[10],"vehicles.":[11],"To":[12],"guarantee":[13],"steady":[14],"power":[15],"supply":[16],"for":[17,66,108,158,171],"train,":[19],"surface":[21,123,138,220],"pantograph":[24,56,78,115,131,150,207],"slide":[25,57,79,116,132,142],"plate":[26,58,80,133],"should":[27],"be":[28,60],"smooth":[29],"enough":[30],"so":[31],"that":[32,213],"catenary":[34],"can":[35,217,249],"move":[36],"on":[37,99,183],"it":[38],"from":[39],"side":[41,45],"to":[42,195],"other":[44],"steadily":[46],"with":[47,230,241],"low":[48],"friction.":[49],"In":[50,90,118,164],"addition,":[51],"thickness":[53],"cannot":[59],"smaller":[61],"than":[62],"lower":[64],"limit":[65],"sake":[68],"safety.":[70],"Therefore,":[71],"periodical":[72],"inspection":[73],"and":[74,87,95,102,126,145,162,200,222,245],"maintenance":[75],"are":[81,143,176,193,239],"significant":[82],"terms":[84],"safe":[86],"stable":[88],"operation.":[89],"this":[91],"paper,":[92],"an":[93],"innovative":[94],"intelligent":[96],"method":[97,128,248],"based":[98,182],"deep":[100,147],"learning":[101,148],"image":[103,184],"processing":[104,185],"technologies":[105],"proposed":[107,215,247],"online":[110],"condition":[111,175,204],"monitoring":[112],"plate.":[117],"first":[120],"stage,":[121,167],"defect":[124,151,160],"detection":[125,152,161],"recognition":[127],"proposed.":[135,177],"Four":[136],"typical":[137],"defects":[139,221,229],"considered,":[144],"a":[146,231],"model,":[149],"neural":[153],"network":[154],"(PDDNet),":[155],"trained":[157],"recognition.":[163],"second":[166],"five":[168],"key":[169],"criteria":[170],"qualifying":[172],"wear":[174,179,198,203,235],"The":[178,209,234],"edge":[180],"estimation":[181,237,252],"technology":[186],"investigated":[188],"detail.":[190],"Furthermore,":[191],"they":[192],"used":[194],"calculate":[196],"depth":[199,236],"evaluate":[201],"slide.":[208],"experiment":[210],"results":[211,238],"demonstrate":[212],"PDDNet":[216],"detect":[218],"also":[223],"recognize":[224],"four":[226],"kinds":[227],"sound":[232],"accuracy.":[233,253],"compared":[240],"on-site":[242],"measurement":[243],"data,":[244],"achieve":[250],"high":[251]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":25},{"year":2022,"cited_by_count":22},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":1}],"updated_date":"2026-08-08T01:25:22.217667","created_date":"2025-10-10T00:00:00"}