{"id":"https://openalex.org/W3168939714","doi":"https://doi.org/10.3233/jifs-210349","title":"RSNet: Rail semantic segmentation network for extracting aerial railroad images","display_name":"RSNet: Rail semantic segmentation network for extracting aerial railroad images","publication_year":2021,"publication_date":"2021-06-01","ids":{"openalex":"https://openalex.org/W3168939714","doi":"https://doi.org/10.3233/jifs-210349","mag":"3168939714"},"language":"en","primary_location":{"id":"doi:10.3233/jifs-210349","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-210349","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent & Fuzzy 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/A5051053332","display_name":"R. S. Rampriya","orcid":null},"institutions":[{"id":"https://openalex.org/I33585257","display_name":"Anna University, Chennai","ror":"https://ror.org/01qhf1r47","country_code":"IN","type":"education","lineage":["https://openalex.org/I33585257"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"R.S. Rampriya","raw_affiliation_strings":["Department of Computer Technology, Anna University (MIT Campus), Chennai, Tamilnadu, The India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Technology, Anna University (MIT Campus), Chennai, Tamilnadu, The India","institution_ids":["https://openalex.org/I33585257"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017653150","display_name":"Sabarinathan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sabarinathan","raw_affiliation_strings":["Couger Inc, Tokyo, The Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Couger Inc, Tokyo, The Japan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101870246","display_name":"R. Suganya","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"R. Suganya","raw_affiliation_strings":["Deparment of Information Technology, Thiagarajar College of Engineering, Madurai, Tamilnadu, The India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Deparment of Information Technology, Thiagarajar College of Engineering, Madurai, Tamilnadu, The India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5051053332"],"corresponding_institution_ids":["https://openalex.org/I33585257"],"apc_list":null,"apc_paid":null,"fwci":0.8025,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.67662774,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"41","issue":"2","first_page":"4051","last_page":"4068"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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":0.9976000189781189,"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/segmentation","display_name":"Segmentation","score":0.7635681629180908},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7386046648025513},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7178716063499451},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7098803520202637},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5413784980773926},{"id":"https://openalex.org/keywords/aerial-image","display_name":"Aerial image","score":0.5320656895637512},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5243097543716431},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.512971818447113},{"id":"https://openalex.org/keywords/drone","display_name":"Drone","score":0.48932191729545593},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.41549280285835266},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.15989723801612854}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7635681629180908},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7386046648025513},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7178716063499451},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7098803520202637},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5413784980773926},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.5320656895637512},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5243097543716431},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.512971818447113},{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.48932191729545593},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.41549280285835266},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.15989723801612854},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jifs-210349","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-210349","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent & Fuzzy Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6100000143051147,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W287601400","https://openalex.org/W2041576219","https://openalex.org/W2054970201","https://openalex.org/W2091622218","https://openalex.org/W2145395493","https://openalex.org/W2594095579","https://openalex.org/W2762439315","https://openalex.org/W2774320778","https://openalex.org/W2793506100","https://openalex.org/W2896910005","https://openalex.org/W2899561758","https://openalex.org/W2948108169","https://openalex.org/W2963881378","https://openalex.org/W2965166097","https://openalex.org/W2977371410","https://openalex.org/W3023432518","https://openalex.org/W3028752951","https://openalex.org/W3044592640","https://openalex.org/W3080625486","https://openalex.org/W3086556255","https://openalex.org/W3096808163","https://openalex.org/W3132942424","https://openalex.org/W6664296413","https://openalex.org/W6754998622","https://openalex.org/W6763132499","https://openalex.org/W6784974481"],"related_works":["https://openalex.org/W4229448053","https://openalex.org/W4247925126","https://openalex.org/W4327774218","https://openalex.org/W2059768187","https://openalex.org/W4312858960","https://openalex.org/W4386036939","https://openalex.org/W4379143281","https://openalex.org/W2605096541","https://openalex.org/W4400597516","https://openalex.org/W4280568880"],"abstract_inverted_index":{"In":[0],"the":[1,20,23,57,63,129,227],"near":[2,48],"future,":[3],"combo":[4],"of":[5,22,59],"UAV":[6,147,208],"(Unmanned":[7],"Aerial":[8],"Vehicle)":[9],"and":[10,61,74,148,161,164,175,192,210,215,234],"computer":[11],"vision":[12],"will":[13],"play":[14],"a":[15,109,135,195,231],"vital":[16],"role":[17],"in":[18,35,42,98,183],"monitoring":[19],"condition":[21],"railroad":[24,36,64,77,99,130,142,185,196],"periodically":[25],"to":[26,56,128,226],"ensure":[27],"passenger":[28],"safety.":[29],"The":[30,188],"most":[31],"significant":[32],"module":[33],"involved":[34],"visual":[37],"processing":[38],"is":[39,45,94,134,159,190],"obstacle":[40,46],"detection,":[41],"which":[43,93,158,170,202],"caution":[44],"fallen":[47],"track":[49],"gage":[50,71],"inside":[51],"or":[52,86],"outside.":[53],"This":[54],"leads":[55],"importance":[58],"detecting":[60],"segment":[62],"as":[65,70,91],"three":[66],"key":[67],"regions,":[68],"such":[69,90],"inside,":[72],"rails,":[73],"background.":[75],"Traditional":[76],"segmentation":[78,169,199],"methods":[79],"depend":[80],"on":[81,105,194,218],"either":[82],"manual":[83],"feature":[84,137,156],"selection":[85],"expensive":[87],"dedicated":[88],"devices":[89],"Lidar,":[92],"typically":[95],"less":[96,177],"reliable":[97],"semantic":[100],"segmentation.":[101],"Also,":[102],"cameras":[103],"mounted":[104],"moving":[106],"vehicles":[107],"like":[108,230],"drone":[110,186],"can":[111],"produce":[112],"high-resolution":[113],"images,":[114],"so":[115],"segmenting":[116,141],"precise":[117],"pixel":[118],"information":[119],"from":[120,206],"those":[121],"aerial":[122,143],"images":[123,144,209],"has":[124],"been":[125],"challenging":[126],"due":[127],"surroundings":[131],"chaos.":[132],"RSNet":[133],"multi-level":[136],"fusion":[138],"algorithm":[139],"for":[140,155,168],"captured":[145],"by":[146],"proposes":[149],"an":[150],"attention-based":[151],"efficient":[152,163],"convolutional":[153],"encoder":[154],"extraction,":[157],"robust":[160],"computationally":[162],"modified":[165],"residual":[166,232,235],"decoder":[167],"considers":[171],"only":[172],"essential":[173],"features":[174],"produces":[176],"overhead":[178],"with":[179],"higher":[180],"performance":[181],"even":[182],"real-time":[184,207],"imagery.":[187],"network":[189],"trained":[191],"tested":[193],"scenic":[197],"view":[198],"dataset":[200],"(RSSD),":[201],"we":[203],"have":[204],"built":[205],"achieves":[211],"0.973":[212],"dice":[213],"coefficient":[214],"0.94":[216],"jaccard":[217],"test":[219],"data":[220],"that":[221],"exhibits":[222],"better":[223],"results":[224],"compared":[225],"existing":[228],"approaches":[229],"unit":[233],"squeeze":[236],"net.":[237]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}