{"id":"https://openalex.org/W7123346384","doi":"https://doi.org/10.1109/tvt.2026.3651888","title":"SFEP-YOLO: A Track Obstacle Detection Model for Autonomous Electric Locomotives in Underground Mine","display_name":"SFEP-YOLO: A Track Obstacle Detection Model for Autonomous Electric Locomotives in Underground Mine","publication_year":2026,"publication_date":"2026-01-12","ids":{"openalex":"https://openalex.org/W7123346384","doi":"https://doi.org/10.1109/tvt.2026.3651888"},"language":null,"primary_location":{"id":"doi:10.1109/tvt.2026.3651888","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2026.3651888","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Vehicular Technology","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/A5122891380","display_name":"Zhangyu Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I257545875","display_name":"Hunan University of Humanities, Science and Technology","ror":"https://ror.org/00s7jmd98","country_code":"CN","type":"education","lineage":["https://openalex.org/I257545875"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhangyu Lu","raw_affiliation_strings":["Hunan University of Humanities, Science and Technology, Loudi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan University of Humanities, Science and Technology, Loudi, China","institution_ids":["https://openalex.org/I257545875"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xizheng Zhang","orcid":"https://orcid.org/0000-0003-2261-5973"},"institutions":[{"id":"https://openalex.org/I257545875","display_name":"Hunan University of Humanities, Science and Technology","ror":"https://ror.org/00s7jmd98","country_code":"CN","type":"education","lineage":["https://openalex.org/I257545875"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xizheng Zhang","raw_affiliation_strings":["Hunan University of Humanities, Science and Technology, Loudi, China"],"raw_orcid":"https://orcid.org/0000-0003-2261-5973","affiliations":[{"raw_affiliation_string":"Hunan University of Humanities, Science and Technology, Loudi, China","institution_ids":["https://openalex.org/I257545875"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122862955","display_name":"Xu Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I27688046","display_name":"Hunan Institute of Engineering","ror":"https://ror.org/03zj2rn70","country_code":"CN","type":"education","lineage":["https://openalex.org/I27688046"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Cao","raw_affiliation_strings":["School of Information Science and Engineering, Hunan Institute of Engineering, Xiangtan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Hunan Institute of Engineering, Xiangtan, China","institution_ids":["https://openalex.org/I27688046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122858830","display_name":"Junying Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I27688046","display_name":"Hunan Institute of Engineering","ror":"https://ror.org/03zj2rn70","country_code":"CN","type":"education","lineage":["https://openalex.org/I27688046"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junying Hou","raw_affiliation_strings":["School of Information Science and Engineering, Hunan Institute of Engineering, Xiangtan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Hunan Institute of Engineering, Xiangtan, China","institution_ids":["https://openalex.org/I27688046"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075069444","display_name":"Xiaofang Yuan","orcid":"https://orcid.org/0000-0001-7280-7207"},"institutions":[{"id":"https://openalex.org/I198357462","display_name":"Changsha University","ror":"https://ror.org/011d8sm39","country_code":"CN","type":"education","lineage":["https://openalex.org/I198357462"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaofang Yuan","raw_affiliation_strings":["National Engineering Research Center for Robotic Visual Perception and Control, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0001-7280-7207","affiliations":[{"raw_affiliation_string":"National Engineering Research Center for Robotic Visual Perception and Control, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I198357462"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":21.9495,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.99422226,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"75","issue":"6","first_page":"9687","last_page":"9700"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10842","display_name":"Railway Engineering and Dynamics","score":0.34540000557899475,"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/T10842","display_name":"Railway Engineering and Dynamics","score":0.34540000557899475,"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/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.1339000016450882,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.09669999778270721,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.6499000191688538},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5939000248908997},{"id":"https://openalex.org/keywords/track","display_name":"Track (disk drive)","score":0.5135999917984009},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4359000027179718},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.38670000433921814},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.3765999972820282},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3709999918937683},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.3260999917984009}],"concepts":[{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.6499000191688538},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5939000248908997},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5486999750137329},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.5135999917984009},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4812000095844269},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4359000027179718},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3901999890804291},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.38670000433921814},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.38420000672340393},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3765999972820282},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31779998540878296},{"id":"https://openalex.org/C79487989","wikidata":"https://www.wikidata.org/wiki/Q934680","display_name":"Vehicle dynamics","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C2777488183","wikidata":"https://www.wikidata.org/wiki/Q6900510","display_name":"Safety monitoring","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C55000061","wikidata":"https://www.wikidata.org/wiki/Q24894777","display_name":"Current sensor","level":3,"score":0.260699987411499},{"id":"https://openalex.org/C6683253","wikidata":"https://www.wikidata.org/wiki/Q7075535","display_name":"Obstacle avoidance","level":4,"score":0.2549999952316284},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2026.3651888","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2026.3651888","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Vehicular Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2096560870","display_name":null,"funder_award_id":"2023JJ50029","funder_id":"https://openalex.org/F4320322843","funder_display_name":"Natural Science Foundation of\u00a0Hunan Province"},{"id":"https://openalex.org/G6082130287","display_name":null,"funder_award_id":"62173134","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6875898876","display_name":null,"funder_award_id":"62473140","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7721339768","display_name":null,"funder_award_id":"62473145","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322843","display_name":"Natural Science Foundation of\u00a0Hunan Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,197,226],"stable":[1],"and":[2,28,50,103,168,211,237,260,281],"safe":[3],"operation":[4],"of":[5,14,24,79,106,146,163,185,293],"autonomous":[6],"mining":[7,194],"electric":[8,287],"locomotives":[9],"(MELs)":[10],"requires":[11],"the":[12,22,57,82,89,109,124,127,136,151,154,160,164,205,208,212,219,223,234,238,246,252,256,261,272,286],"support":[13],"advanced":[15],"track":[16,186],"obstacle":[17],"detection":[18,26,30,36,66,172,180,262,270],"technology.":[19],"Due":[20],"to":[21,61,91,116,133,142,203,217],"problems":[23],"slow":[25],"speed":[27,263,280],"low":[29],"accuracy":[31,173,247],"in":[32,70,88,140,297],"traditional":[33,58],"sensor":[34],"based":[35],"methods,":[37],"Swin":[38,83,96],"Transformer,":[39],"Focal":[40,110],"EIOU":[41,111],"loss":[42,112],"function,":[43],"Efficient":[44],"Channel":[45,95],"Attention":[46],"(ECA)":[47],"attention":[48,129],"mechanism,":[49],"four":[51,155],"prediction":[52,156,166],"layers":[53],"were":[54,215],"introduced":[55,87,115],"into":[56],"YOLO":[59],"model":[60,67,85,229,274],"develop":[62],"a":[63,93,276],"high-precision":[64],"obstacles":[65,187,240,294],"for":[68,120,174,222],"MELs":[69,239],"this":[71],"work,":[72],"which":[73,99,283],"is":[74,250,254,258,264],"called":[75],"SFEP-YOLO.":[76],"In":[77,123,150],"terms":[78],"network":[80],"structure,":[81],"Transformer":[84],"was":[86,114,131,201,230],"Backbone":[90],"design":[92],"Multi-scale":[94],"Transformer(MCST)":[97],"module,":[98],"achieves":[100,275],"windowed":[101],"extraction":[102],"interactive":[104],"calculation":[105],"image":[107],"features;":[108],"function":[113],"improve":[117],"recognition":[118],"ability":[119,162],"difficult-to-classify":[121],"samples.":[122],"Neck":[125],"layer,":[126,153],"ECA":[128],"mechanism":[130],"incorporated":[132],"effectively":[134],"capture":[135],"interdependence":[137],"between":[138,279],"channels,":[139],"order":[141],"achieve":[143],"rapid":[144],"localization":[145],"important":[147],"feature":[148],"information.":[149],"Head":[152],"layer":[157],"structure":[158],"enhances":[159],"fitting":[161],"model's":[165],"box":[167],"true":[169],"box,":[170],"providing":[171],"small":[175],"obstacles,":[176],"maintaining":[177],"high":[178],"performance":[179],"accuracy.":[181],"A":[182],"rich":[183],"dataset":[184,236],"has":[188],"been":[189],"established":[190],"from":[191],"real":[192,298],"complex":[193],"transportation":[195],"scenes.":[196],"transfer":[198],"learning":[199],"method":[200],"adopted":[202],"train":[204],"backbone":[206],"on":[207,233],"VisDrone2019":[209,235],"dataset,":[210],"obtained":[213],"weights":[214],"set":[216],"be":[218],"pre-training":[220],"parameters":[221],"SFEP-YOLO":[224,228,273],"model.":[225],"proposed":[227],"tested":[231],"both":[232],"dataset.":[241],"Experimental":[242],"results":[243],"show":[244],"that":[245,285],"at":[248],"mAP@50":[249],"93.8%,":[251],"Precision":[253],"93.7%,":[255],"Recall":[257],"91.2%,":[259],"117FPS.":[265],"Compared":[266],"with":[267],"other":[268],"existing":[269],"models,":[271],"good":[277],"balance":[278],"accuracy,":[282],"ensures":[284],"locomotive":[288],"can":[289],"detect":[290],"all":[291],"kinds":[292],"while":[295],"detecting":[296],"time.":[299]},"counts_by_year":[{"year":2026,"cited_by_count":5}],"updated_date":"2026-06-23T06:36:01.041984","created_date":"2026-01-14T00:00:00"}