{"id":"https://openalex.org/W4413344123","doi":"https://doi.org/10.1109/tits.2025.3598982","title":"YOLOv8DTL: A Deep Transfer Learning Model for Few-Shot Rail Abrasion Detection","display_name":"YOLOv8DTL: A Deep Transfer Learning Model for Few-Shot Rail Abrasion Detection","publication_year":2025,"publication_date":"2025-08-19","ids":{"openalex":"https://openalex.org/W4413344123","doi":"https://doi.org/10.1109/tits.2025.3598982"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2025.3598982","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3598982","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/A5091342354","display_name":"Zhenyu Zhang","orcid":"https://orcid.org/0000-0003-0445-2082"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]},{"id":"https://openalex.org/I4210165204","display_name":"Zhuhai Institute of Advanced Technology","ror":"https://ror.org/05r1mzq61","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761","https://openalex.org/I4210165204"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhenyu Zhang","raw_affiliation_strings":["Beijing Institute of Technology, Zhuhai, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Zhuhai, China","institution_ids":["https://openalex.org/I4210165204","https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028933793","display_name":"Hui Zhao","orcid":"https://orcid.org/0000-0003-0862-3846"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Zhao","raw_affiliation_strings":["Department of Computer Science and Technology, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","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, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5091342354"],"corresponding_institution_ids":["https://openalex.org/I125839683","https://openalex.org/I4210165204"],"apc_list":null,"apc_paid":null,"fwci":0.9078,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.76705147,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"26","issue":"11","first_page":"20202","last_page":"20211"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12482","display_name":"Tunneling and Rock Mechanics","score":0.9882000088691711,"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/T12482","display_name":"Tunneling and Rock Mechanics","score":0.9882000088691711,"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/T12427","display_name":"Metal Alloys Wear and Properties","score":0.9739000201225281,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9505000114440918,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6551427841186523},{"id":"https://openalex.org/keywords/abrasion","display_name":"Abrasion (mechanical)","score":0.6007271409034729},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47920140624046326},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4671791195869446},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4645170271396637},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.4394074082374573},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.35819652676582336},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.3221778869628906},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.3180777430534363},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.25785431265830994},{"id":"https://openalex.org/keywords/metallurgy","display_name":"Metallurgy","score":0.1307903230190277}],"concepts":[{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6551427841186523},{"id":"https://openalex.org/C118231568","wikidata":"https://www.wikidata.org/wiki/Q3819233","display_name":"Abrasion (mechanical)","level":2,"score":0.6007271409034729},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47920140624046326},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4671791195869446},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4645170271396637},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.4394074082374573},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.35819652676582336},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3221778869628906},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.3180777430534363},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.25785431265830994},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.1307903230190277}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2025.3598982","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3598982","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/G3186223970","display_name":null,"funder_award_id":"52302436","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1500049967","https://openalex.org/W2006269603","https://openalex.org/W2063045209","https://openalex.org/W2242952562","https://openalex.org/W2598457882","https://openalex.org/W2601564443","https://openalex.org/W2761216034","https://openalex.org/W2768955070","https://openalex.org/W2790914279","https://openalex.org/W2792121133","https://openalex.org/W2800079217","https://openalex.org/W2803930526","https://openalex.org/W2890733150","https://openalex.org/W2939355860","https://openalex.org/W2979396152","https://openalex.org/W3009013436","https://openalex.org/W3011150543","https://openalex.org/W3013695998","https://openalex.org/W3033645921","https://openalex.org/W3037921770","https://openalex.org/W3112721793","https://openalex.org/W3132135025","https://openalex.org/W3138505474","https://openalex.org/W3152253470","https://openalex.org/W3171297660","https://openalex.org/W3177456145","https://openalex.org/W3179632985","https://openalex.org/W3199083432","https://openalex.org/W3202041989","https://openalex.org/W3208486706","https://openalex.org/W4200478585","https://openalex.org/W4205752477","https://openalex.org/W4223625941","https://openalex.org/W4226027146","https://openalex.org/W4282837177","https://openalex.org/W4285505265","https://openalex.org/W4285676267","https://openalex.org/W4289752563","https://openalex.org/W4295308303","https://openalex.org/W4297860879","https://openalex.org/W4309694769","https://openalex.org/W4320478490","https://openalex.org/W4385741117","https://openalex.org/W4386852236","https://openalex.org/W4391001683","https://openalex.org/W4392611940","https://openalex.org/W4392904527"],"related_works":["https://openalex.org/W4206357785","https://openalex.org/W4281381188","https://openalex.org/W2951211570","https://openalex.org/W3192840557","https://openalex.org/W4375928479","https://openalex.org/W3167935049","https://openalex.org/W3023427754","https://openalex.org/W3131673289","https://openalex.org/W4393011546","https://openalex.org/W3198847674"],"abstract_inverted_index":{"Rapid":[0],"movement":[1],"of":[2,24,32,39,43,50,63,88,126,178],"the":[3,22,48,61,86,113,124,135,139,171],"wheels":[4],"on":[5,74],"defective":[6],"tracks":[7],"and":[8,16,27,36,101,128,147],"high-frequency":[9],"friction":[10],"collisions":[11],"cause":[12,31],"vibration":[13],"between":[14],"train":[15,25,33],"rail":[17,40,56,82],"track,":[18],"which":[19],"greatly":[20],"damages":[21],"lifespan":[23],"components":[26],"is":[28,42,78,158],"a":[29,68,102,175],"significant":[30],"derailments.":[34],"Timely":[35],"accurate":[37],"detection":[38,58,114,137,162,179],"abrasions":[41,83],"great":[44],"significance":[45],"for":[46],"ensuring":[47],"safety":[49],"railway":[51],"operations.":[52],"Deep":[53],"learning":[54,71],"based-automatic":[55],"abrasion":[57,130,152,166],"methods":[59],"face":[60],"challenge":[62],"having":[64],"fewer":[65],"samples.":[66],"Thus,":[67],"deep":[69],"transfer":[70],"framework":[72],"based":[73],"improved":[75],"YOLOv8":[76],"models":[77],"developed":[79,140],"to":[80,84,111,161],"detect":[81],"address":[85],"issue":[87],"few-shot":[89],"learning.":[90],"Then,":[91],"deformable":[92],"convolution":[93],"networks":[94],"(DCNs),":[95],"convolutional":[96],"block":[97],"attention":[98],"module":[99],"(CBAM)":[100],"new":[103],"intersection":[104],"over":[105],"union":[106],"(IoU)":[107],"loss":[108],"are":[109],"introduced":[110],"improve":[112],"performance.":[115],"The":[116],"ablation":[117],"experiments":[118],"show":[119],"that":[120,156],"it":[121,157],"effectively":[122],"reduces":[123],"rate":[125],"missed":[127],"false":[129],"detections.":[131],"By":[132],"comparing":[133],"with":[134,164],"existing":[136],"methods,":[138],"YOLOv8DTL":[141],"method":[142],"has":[143,170],"higher":[144],"precision,":[145],"recall,":[146],"average":[148],"precision":[149],"under":[150],"different":[151,165],"size":[153],"thresholds,":[154],"indicating":[155],"more":[159],"adaptable":[160],"tasks":[163],"sizes.":[167],"It":[168],"also":[169],"best":[172],"robustness,":[173],"maintaining":[174],"high":[176],"level":[177],"efficiency.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
