{"id":"https://openalex.org/W4415626611","doi":"https://doi.org/10.1109/tits.2025.3618979","title":"SRLF: Sparse Representation Learning Framework for Railroad Surrounding Potential Risk Perception Using UAV Imagery","display_name":"SRLF: Sparse Representation Learning Framework for Railroad Surrounding Potential Risk Perception Using UAV Imagery","publication_year":2025,"publication_date":"2025-10-28","ids":{"openalex":"https://openalex.org/W4415626611","doi":"https://doi.org/10.1109/tits.2025.3618979"},"language":null,"primary_location":{"id":"doi:10.1109/tits.2025.3618979","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3618979","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/A5103176994","display_name":"Fanteng Meng","orcid":"https://orcid.org/0009-0007-6276-7708"},"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":true,"raw_author_name":"Fanteng Meng","raw_affiliation_strings":["State Key Laboratory of Advanced Rail Autonomous Operation and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Advanced Rail Autonomous Operation and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088716214","display_name":"Yong Qin","orcid":"https://orcid.org/0000-0002-6519-8316"},"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":"Yong Qin","raw_affiliation_strings":["State Key Laboratory of Advanced Rail Autonomous Operation and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Advanced Rail Autonomous Operation and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090506911","display_name":"Yunpeng Wu","orcid":"https://orcid.org/0000-0002-8094-7610"},"institutions":[{"id":"https://openalex.org/I10660446","display_name":"Kunming University of Science and Technology","ror":"https://ror.org/00xyeez13","country_code":"CN","type":"education","lineage":["https://openalex.org/I10660446"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunpeng Wu","raw_affiliation_strings":["Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, China","institution_ids":["https://openalex.org/I10660446"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069453458","display_name":"Mingyang Chen","orcid":"https://orcid.org/0009-0001-3882-0878"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Mingyang Chen","raw_affiliation_strings":["Department of Electronic and Electrical Engineering, University College London, London, U.K"],"affiliations":[{"raw_affiliation_string":"Department of Electronic and Electrical Engineering, University College London, London, U.K","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020749901","display_name":"Ninghai Qiu","orcid":"https://orcid.org/0009-0009-7203-7631"},"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":"Ninghai Qiu","raw_affiliation_strings":["State Key Laboratory of Advanced Rail Autonomous Operation and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Advanced Rail Autonomous Operation and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100424175","display_name":"Zhipeng Wang","orcid":"https://orcid.org/0000-0003-3039-7582"},"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":"Zhipeng Wang","raw_affiliation_strings":["State Key Laboratory of Advanced Rail Autonomous Operation and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Advanced Rail Autonomous Operation and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075985115","display_name":"Chongchong Yu","orcid":"https://orcid.org/0000-0003-4234-1260"},"institutions":[{"id":"https://openalex.org/I179026463","display_name":"Beijing Technology and Business University","ror":"https://ror.org/013e0zm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I179026463"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chongchong Yu","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Technology and Business University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Technology and Business University, Beijing, China","institution_ids":["https://openalex.org/I179026463"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106546994","display_name":"Huaizhi Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I2801441622","display_name":"China Railway Corporation","ror":"https://ror.org/044wv3489","country_code":"CN","type":"government","lineage":["https://openalex.org/I2801441622","https://openalex.org/I4210122102"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huaizhi Yang","raw_affiliation_strings":["Beijing–Shanghai High Speed Railway Company Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing–Shanghai High Speed Railway Company Ltd., Beijing, China","institution_ids":["https://openalex.org/I2801441622"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5103176994"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31993648,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"26","issue":"12","first_page":"22076","last_page":"22093"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.35370001196861267,"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":0.35370001196861267,"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":0.1754000037908554,"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/T11133","display_name":"UAV Applications and Optimization","score":0.061900001019239426,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/discriminative-model","display_name":"Discriminative model","score":0.5928000211715698},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5532000064849854},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5432999730110168},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5357999801635742},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4618000090122223},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.45570001006126404},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.45159998536109924},{"id":"https://openalex.org/keywords/aerial-imagery","display_name":"Aerial imagery","score":0.4359999895095825},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.430400013923645}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7181000113487244},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6815999746322632},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5928000211715698},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5532000064849854},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5432999730110168},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5357999801635742},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4618000090122223},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.45570001006126404},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.45159998536109924},{"id":"https://openalex.org/C2987819851","wikidata":"https://www.wikidata.org/wiki/Q191839","display_name":"Aerial imagery","level":2,"score":0.4359999895095825},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.430400013923645},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4255000054836273},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41019999980926514},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.40400001406669617},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.38029998540878296},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.37389999628067017},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3617999851703644},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3472000062465668},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.3456999957561493},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.30799999833106995},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2874000072479248},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.27309998869895935},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.2667999863624573},{"id":"https://openalex.org/C13670688","wikidata":"https://www.wikidata.org/wiki/Q3500548","display_name":"Space partitioning","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2025.3618979","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3618979","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":[],"awards":[{"id":"https://openalex.org/G3814781215","display_name":null,"funder_award_id":"RAO2025ZZ005","funder_id":"https://openalex.org/F4320329246","funder_display_name":"State Key Laboratory of Advanced Metallurgy"},{"id":"https://openalex.org/G4217964824","display_name":null,"funder_award_id":"202401AT070409","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4405684913","display_name":null,"funder_award_id":"52362048","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G523458086","display_name":null,"funder_award_id":"2024QYBS033","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G8186894268","display_name":null,"funder_award_id":"202301BE070001-042","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8690858758","display_name":null,"funder_award_id":"RAO2023ZZ003","funder_id":"https://openalex.org/F4320329246","funder_display_name":"State Key Laboratory of Advanced Metallurgy"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320329246","display_name":"State Key Laboratory of Advanced Metallurgy","ror":null},{"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":34,"referenced_works":["https://openalex.org/W2097117768","https://openalex.org/W2194775991","https://openalex.org/W2618530766","https://openalex.org/W2948982773","https://openalex.org/W2990559999","https://openalex.org/W2993182889","https://openalex.org/W3035396860","https://openalex.org/W3035524453","https://openalex.org/W3084953805","https://openalex.org/W3105515746","https://openalex.org/W3168939714","https://openalex.org/W3176709420","https://openalex.org/W3177456145","https://openalex.org/W3183588514","https://openalex.org/W3217535672","https://openalex.org/W4205098444","https://openalex.org/W4309224975","https://openalex.org/W4311425951","https://openalex.org/W4313018996","https://openalex.org/W4362553393","https://openalex.org/W4364320749","https://openalex.org/W4385254037","https://openalex.org/W4386066015","https://openalex.org/W4386076022","https://openalex.org/W4387490603","https://openalex.org/W4387674460","https://openalex.org/W4391908171","https://openalex.org/W4394625793","https://openalex.org/W4399068800","https://openalex.org/W4400215447","https://openalex.org/W4401494318","https://openalex.org/W4401502907","https://openalex.org/W4403770406","https://openalex.org/W4404788114"],"related_works":[],"abstract_inverted_index":{"Regular":[0],"inspection":[1],"of":[2,143],"potential":[3],"risks":[4,36,58],"in":[5],"railroad":[6,131],"surroundings":[7,132],"is":[8],"essential":[9],"for":[10,152],"operational":[11],"safety.":[12],"Uncrewed":[13],"aerial":[14,22],"vehicles":[15],"(UAVs)":[16],"offer":[17],"an":[18],"effective":[19],"solution":[20],"with":[21,31],"mobility":[23],"and":[24,41,66,79,119,156],"long-distance":[25],"coverage.":[26],"However,":[27],"existing":[28],"methods":[29],"struggle":[30],"rare":[32],"but":[33],"extremely":[34],"high":[35,140],"characterized":[37],"by":[38],"limited":[39],"samples":[40],"complex":[42],"feature":[43],"distributions.":[44],"To":[45],"address":[46],"this,":[47],"we":[48],"propose":[49],"SRLF":[50,136],"(Sparse":[51],"Representation":[52],"Learning":[53,71],"Framework),":[54],"which":[55],"decomposes":[56],"sparse":[57],"(SR)":[59],"perception":[60],"into":[61],"three":[62],"components:":[63],"capture,":[64],"excavation,":[65],"learning.":[67],"First,":[68],"Buffer":[69],"Decouple":[70],"(BDL)":[72],"decouples":[73],"objectness":[74],"from":[75,93],"classification":[76],"to":[77,97,112],"capture":[78],"enhance":[80],"foreground":[81],"perception.":[82],"Second,":[83],"Feature":[84],"Space":[85],"Dynamic":[86],"Sampling":[87],"(FSDS)":[88],"leverages":[89],"adaptive":[90],"quantity":[91],"sampling":[92],"multivariate":[94],"Gaussian":[95],"distributions":[96],"excavate":[98],"discriminative":[99],"SR":[100],"representations.":[101],"Third,":[102],"Triple":[103],"Similarity":[104],"Loss":[105],"(TSL)":[106],"constructs":[107],"a":[108,139],"triple":[109],"comparison":[110],"mechanism":[111],"contrastively":[113],"shape":[114],"uncertainty":[115],"surfaces":[116],"between":[117],"SRs":[118,153],"common":[120],"safety":[121],"hazards":[122],"(CSHs).":[123],"Finally,":[124],"extensive":[125],"experiments":[126],"conducted":[127],"on":[128],"the":[129],"UAV-based":[130],"dataset":[133],"demonstrate":[134],"that":[135],"can":[137],"achieve":[138],"detection":[141],"rate":[142,151],"CSHs":[144],"(95.6%":[145],"mAP)":[146],"while":[147],"maintaining":[148],"low":[149],"miss-detection":[150],"(81.9%":[154],"Recall":[155],"0.5%":[157],"FPR95).":[158]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-29T00:00:00"}