{"id":"https://openalex.org/W3115574947","doi":"https://doi.org/10.1109/itsc45102.2020.9294755","title":"Robust Train Component Detection with Cascade Convolutional Neural Networks based on Structure Knowledge","display_name":"Robust Train Component Detection with Cascade Convolutional Neural Networks based on Structure Knowledge","publication_year":2020,"publication_date":"2020-09-20","ids":{"openalex":"https://openalex.org/W3115574947","doi":"https://doi.org/10.1109/itsc45102.2020.9294755","mag":"3115574947"},"language":"en","primary_location":{"id":"doi:10.1109/itsc45102.2020.9294755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294755","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","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/A5089026231","display_name":"Zhongyao Cheng","orcid":"https://orcid.org/0009-0006-2363-450X"},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Cheng Zhongyao","raw_affiliation_strings":["Institute for Infocomm Research, A*star, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, A*star, Singapore","institution_ids":["https://openalex.org/I3005327000"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101098798","display_name":"Zhu Juelin","orcid":null},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhu Juelin","raw_affiliation_strings":["Hunan University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan University, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100622590","display_name":"Cen Chen","orcid":"https://orcid.org/0000-0003-1389-0148"},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Chen Cen","raw_affiliation_strings":["Institute for Infocomm Research, A*star, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, A*star, Singapore","institution_ids":["https://openalex.org/I3005327000"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101396977","display_name":"Xiaoxi Yu","orcid":"https://orcid.org/0000-0001-5387-5677"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yu Xiaoxi","raw_affiliation_strings":["National University of Singapore, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081439040","display_name":"Fan Wu","orcid":"https://orcid.org/0000-0001-9392-2597"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wu Fan","raw_affiliation_strings":["Hunan University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan University, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101681732","display_name":"Yue Li","orcid":"https://orcid.org/0000-0001-5368-8038"},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Li Yue","raw_affiliation_strings":["Institute for Infocomm Research, A*star, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, A*star, Singapore","institution_ids":["https://openalex.org/I3005327000"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010092389","display_name":"Zeng Zeng","orcid":"https://orcid.org/0000-0002-2405-0323"},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Zeng Zeng","raw_affiliation_strings":["Institute for Infocomm Research, A*star, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, A*star, Singapore","institution_ids":["https://openalex.org/I3005327000"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.15715383,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9947999715805054,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9847000241279602,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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":0.8199536204338074},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.7309458255767822},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7138504385948181},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.7078225612640381},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6991105675697327},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6326725482940674},{"id":"https://openalex.org/keywords/cascade","display_name":"Cascade","score":0.5761780142784119},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5002965927124023},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47356879711151123},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43917155265808105},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.42930740118026733},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3632775545120239},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07876408100128174}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8199536204338074},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.7309458255767822},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7138504385948181},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.7078225612640381},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6991105675697327},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6326725482940674},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.5761780142784119},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5002965927124023},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47356879711151123},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43917155265808105},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.42930740118026733},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3632775545120239},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07876408100128174},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C42360764","wikidata":"https://www.wikidata.org/wiki/Q83588","display_name":"Chemical engineering","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/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc45102.2020.9294755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294755","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.550000011920929,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W571507881","https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1861492603","https://openalex.org/W1932847118","https://openalex.org/W2084085693","https://openalex.org/W2102605133","https://openalex.org/W2163605009","https://openalex.org/W2565639579","https://openalex.org/W2579152745","https://openalex.org/W2610420510","https://openalex.org/W2613718673","https://openalex.org/W2625219738","https://openalex.org/W2796347433","https://openalex.org/W2799215407","https://openalex.org/W2809371442","https://openalex.org/W2919115771","https://openalex.org/W2953755354","https://openalex.org/W2963037989","https://openalex.org/W2963058975","https://openalex.org/W2963351448","https://openalex.org/W2963542991","https://openalex.org/W2964241181","https://openalex.org/W2986095360","https://openalex.org/W3102262568","https://openalex.org/W3106250896","https://openalex.org/W4293584584","https://openalex.org/W6616210543","https://openalex.org/W6629368666","https://openalex.org/W6631782140","https://openalex.org/W6684191040","https://openalex.org/W6750227808","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W2949096641","https://openalex.org/W2970686063","https://openalex.org/W2969228573","https://openalex.org/W4320729701"],"abstract_inverted_index":{"Recently,":[0],"convolutional":[1],"neural":[2],"network":[3],"(CNN)":[4],"based":[5,151],"methods":[6],"have":[7,16,49,216],"achieved":[8],"superior":[9],"results":[10],"in":[11,21,44,172],"generic":[12],"object":[13],"detection":[14,64],"and":[15,52,57,95,116,121,130,173,201,228],"become":[17],"the":[18,22,40,46,63,72,101,114,124,153,158,162,176,188,221],"de-facto":[19],"standard":[20],"domain.":[23],"However,":[24],"potential":[25],"adaptations":[26],"to":[27,112,132,186,235],"industrial":[28],"areas":[29,125,168],"are":[30,59,169,199,210],"not":[31],"well":[32],"studied":[33],"yet.":[34],"A":[35],"case":[36],"worth":[37],"exploring":[38],"is":[39,111],"train":[41,68,76,87,104,208],"component":[42,77,88,119],"detection,":[43],"which":[45,204],"components":[47,54,69,134,209],"may":[48,127],"strong":[50],"relationships":[51,102],"some":[53],"(e.g.,":[55],"screws":[56],"nuts)":[58],"very":[60],"small.":[61],"Nevertheless,":[62],"performance":[65],"of":[66,74,100,109,148,190,196],"small":[67,129],"significantly":[70],"affects":[71],"efficiency":[73],"overall":[75],"detection.":[78,183],"In":[79,184],"this":[80],"work,":[81],"we":[82],"propose":[83],"a":[84],"novel":[85],"robust":[86],"detection(RTCD)":[89],"framework,":[90],"built":[91],"on":[92,152],"cascading":[93],"CNNs":[94,160],"utilizing":[96],"prior":[97],"structure":[98,164],"knowledge":[99],"between":[103],"components.":[105],"The":[106,226],"core":[107],"idea":[108],"RTCD":[110,219],"detect":[113,133],"big":[115],"easily":[117],"detectable":[118],"first,":[120],"then":[122],"find":[123,145],"that":[126,218],"contain":[128],"challenging":[131],"for":[135,181],"following":[136,177],"fine-grained":[137],"exploitation.":[138],"Our":[139],"proposed":[140],"attention":[141],"region":[142],"mechanism":[143],"can":[144],"regions":[146],"deserving":[147],"further":[149,182],"analysis":[150],"region-of-interest":[154],"(ROI)":[155],"detected":[156],"by":[157],"previous":[159],"with":[161],"known":[163],"knowledge.":[165],"Then,":[166],"these":[167],"cropped,":[170],"zoomed":[171],"fed":[174],"into":[175],"deep":[178],"learning":[179],"models":[180],"order":[185],"verify":[187],"effectiveness":[189],"RTCD,":[191],"1,":[192],"130":[193],"high-resolution":[194],"images":[195],"moving":[197],"trains":[198],"captured":[200],"collected,":[202],"from":[203],"17,":[205],"334":[206],"critical":[207],"manually":[211],"annotated.":[212],"Extensive":[213],"experiments":[214],"therein":[215],"demonstrated":[217],"outperforms":[220],"existing":[222],"state-of-the-art":[223],"baselines":[224],"significantly.":[225],"dataset":[227],"corresponding":[229],"source":[230],"code":[231],"will":[232],"be":[233],"released":[234],"facilitate":[236],"more":[237],"future":[238],"work.":[239]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
