{"id":"https://openalex.org/W7223929682","doi":"https://doi.org/10.3390/sym18111782","title":"OSLTSF-YOLO: Oriented Rail Surface Defect Detection via Orientation-Adaptive Slender-Strip Receptive Fields and Local Texture-Aware Scale Fusion","display_name":"OSLTSF-YOLO: Oriented Rail Surface Defect Detection via Orientation-Adaptive Slender-Strip Receptive Fields and Local Texture-Aware Scale Fusion","publication_year":2026,"publication_date":"2026-10-26","ids":{"openalex":"https://openalex.org/W7223929682","doi":"https://doi.org/10.3390/sym18111782"},"language":"en","primary_location":{"id":"doi:10.3390/sym18111782","doi":"https://doi.org/10.3390/sym18111782","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym18111782","pdf_url":null,"source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/sym18111782","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020607290","display_name":"Qiaolu Wang","orcid":"https://orcid.org/0009-0000-5768-4018","observed_orcids":["https://orcid.org/0009-0000-5768-4018"]},"institutions":[{"id":"https://openalex.org/I4405279637","display_name":"Sichuan Technology and Business University","ror":"https://ror.org/01sr0fc56","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405279637"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiaolu Wang","raw_affiliation_strings":["School of Artificial Intelligence and Electronic Engineering, Sichuan Technology and Business University, Chengdu 611745, China"],"raw_orcid":"https://orcid.org/0009-0000-5768-4018","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Electronic Engineering, Sichuan Technology and Business University, Chengdu 611745, China","institution_ids":["https://openalex.org/I4405279637"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5159952606","display_name":"Bin Shi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405279637","display_name":"Sichuan Technology and Business University","ror":"https://ror.org/01sr0fc56","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405279637"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Bin Shi","raw_affiliation_strings":["School of Artificial Intelligence and Electronic Engineering, Sichuan Technology and Business University, Chengdu 611745, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Electronic Engineering, Sichuan Technology and Business University, Chengdu 611745, China","institution_ids":["https://openalex.org/I4405279637"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5159952606"],"corresponding_institution_ids":["https://openalex.org/I4405279637"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2896},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2896},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.7849898,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":"11","first_page":"1782","last_page":"1782"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.4699999988079071,"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"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.4699999988079071,"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":0.44359999895095825,"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":0.08370000123977661,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/rail-surface-defect-detection","display_name":"rail surface defect detection","score":0.9769999980926514},{"id":"https://openalex.org/keywords/oriented-bounding-boxes","display_name":"oriented bounding boxes","score":0.906000018119812},{"id":"https://openalex.org/keywords/strip-convolution","display_name":"strip convolution","score":0.6179999709129333},{"id":"https://openalex.org/keywords/lightweight-object-detection","display_name":"lightweight object detection","score":0.8050000071525574},{"id":"https://openalex.org/keywords/yolo11","display_name":"YOLOv11","score":0.8610000014305115}],"concepts":[{"id":"https://openalex.org/C130367717","wikidata":"https://www.wikidata.org/wiki/Q189791","display_name":"Diagonal","level":2,"score":0.6766999959945679},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6492000222206116},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5494999885559082},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4778999984264374},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.47290000319480896},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4643000066280365},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.4537999927997589},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.4410000145435333},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42260000109672546},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.4000000059604645},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.39070001244544983},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.3610000014305115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3472999930381775},{"id":"https://openalex.org/C9395851","wikidata":"https://www.wikidata.org/wiki/Q177929","display_name":"Stack (abstract data type)","level":2,"score":0.3431999981403351},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.3393999934196472},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.33079999685287476},{"id":"https://openalex.org/C40636538","wikidata":"https://www.wikidata.org/wiki/Q81392","display_name":"Fractal","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.31940001249313354},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C135598885","wikidata":"https://www.wikidata.org/wiki/Q1366302","display_name":"Row","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.29679998755455017},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C2778134712","wikidata":"https://www.wikidata.org/wiki/Q1047307","display_name":"Bundle","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.2687999904155731},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C71039073","wikidata":"https://www.wikidata.org/wiki/Q3439090","display_name":"Surface finish","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C152588345","wikidata":"https://www.wikidata.org/wiki/Q6498959","display_name":"Scale model","level":2,"score":0.2587999999523163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3390/sym18111782","doi":"https://doi.org/10.3390/sym18111782","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym18111782","pdf_url":null,"source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3390/sym18111782","doi":"https://doi.org/10.3390/sym18111782","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym18111782","pdf_url":null,"source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2565639579","https://openalex.org/W2761216034","https://openalex.org/W2962749812","https://openalex.org/W2962793481","https://openalex.org/W2963351448","https://openalex.org/W2981609437","https://openalex.org/W2982770724","https://openalex.org/W3011965665","https://openalex.org/W3034502973","https://openalex.org/W3035396860","https://openalex.org/W3042957710","https://openalex.org/W3159160396","https://openalex.org/W3171297660","https://openalex.org/W3174873843","https://openalex.org/W3201828441","https://openalex.org/W3204204809","https://openalex.org/W4390660421","https://openalex.org/W4390873076","https://openalex.org/W4402754006","https://openalex.org/W4402865423","https://openalex.org/W4416798013","https://openalex.org/W4417470602","https://openalex.org/W7161473569","https://openalex.org/W7165016393","https://openalex.org/W7168276466","https://openalex.org/W7169818525"],"related_works":["https://openalex.org/W7128798484","https://openalex.org/W7168200567","https://openalex.org/W7167205294","https://openalex.org/W4400225115","https://openalex.org/W7213214112","https://openalex.org/W4380995530","https://openalex.org/W4381662712","https://openalex.org/W7133339193","https://openalex.org/W7140145427","https://openalex.org/W4407694149"],"abstract_inverted_index":{"Rail":[0],"surface":[1],"defects":[2],"frequently":[3],"exhibit":[4],"arbitrary":[5],"orientations,":[6],"elongated":[7,74],"geometries,":[8],"small":[9],"spatial":[10,115],"footprints,":[11],"and":[12,79,109,152,159,166,169,182],"substantial":[13],"texture-scale":[14],"variation.":[15],"Although":[16],"oriented":[17,52,187],"bounding":[18],"boxes":[19],"(OBBs)":[20],"improve":[21],"geometric":[22],"localization,":[23],"they":[24],"do":[25],"not":[26],"directly":[27],"address":[28],"insufficient":[29],"directional-structure":[30],"modeling":[31,177],"in":[32,43],"the":[33,36,44,68,98,142],"backbone":[34,69],"or":[35],"limited":[37],"adaptability":[38],"of":[39,150,154,178],"fixed-scale":[40],"feature":[41],"fusion":[42,92],"neck.":[45],"This":[46],"study":[47],"proposes":[48],"OSLTSF-YOLO,":[49],"a":[50,146],"lightweight":[51,186],"detector":[53],"built":[54],"on":[55],"YOLO11n-OBB.":[56],"An":[57],"orientation-adaptive":[58],"slender":[59],"strip":[60],"receptive":[61,180],"field":[62],"(OSRF)":[63],"module":[64,94],"is":[65,95],"inserted":[66],"at":[67,97,131],"P4":[70,100],"level":[71,101],"to":[72,102,126],"encode":[73],"structures":[75],"through":[76],"horizontal,":[77],"vertical,":[78],"two":[80],"diagonal":[81],"strip-convolution":[82],"branches":[83],"with":[84,137,157],"pixel-wise":[85],"directional":[86,179],"routing.":[87],"A":[88],"local":[89,110],"texture-aware":[90],"scale":[91],"(LTSF)":[93],"introduced":[96],"neck":[99],"combine":[103],"multi-dilation":[104],"depthwise":[105],"features":[106,130],"using":[107,162],"global":[108],"texture":[111],"routing":[112],"followed":[113],"by":[114],"response":[116],"modulation.":[117],"Both":[118],"modules":[119],"use":[120],"small-magnitude":[121],"non-zero":[122],"bounded":[123],"residual":[124],"injection":[125],"preserve":[127],"their":[128],"input":[129],"initialization.":[132],"On":[133],"an":[134,138],"in-house":[135],"dataset":[136],"image-level":[139],"random":[140],"split,":[141],"complete":[143],"model":[144],"achieved":[145],"three-seed":[147],"mean":[148],"mAP50":[149],"88.47%":[151],"F1":[153],"87.91%,":[155],"compared":[156],"83.91%":[158],"84.94%,":[160],"while":[161],"3.17":[163],"M":[164],"parameters":[165],"6.86":[167],"GFLOPs":[168],"achieving":[170],"118.0":[171],"FPS.":[172],"These":[173],"results":[174],"support":[175],"structured":[176],"fields":[181],"texture-dependent":[183],"scales":[184],"for":[185],"rail":[188],"defect":[189],"detection.":[190]},"counts_by_year":[],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2026-10-28T00:00:00"}