{"id":"https://openalex.org/W4388628844","doi":"https://doi.org/10.14311/nnw.2023.33.019","title":"Towards the development of obstacle detection in railway tracks using thermal imaging","display_name":"Towards the development of obstacle detection in railway tracks using thermal imaging","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4388628844","doi":"https://doi.org/10.14311/nnw.2023.33.019"},"language":"en","primary_location":{"id":"doi:10.14311/nnw.2023.33.019","doi":"https://doi.org/10.14311/nnw.2023.33.019","is_oa":false,"landing_page_url":"https://doi.org/10.14311/nnw.2023.33.019","pdf_url":null,"source":{"id":"https://openalex.org/S206406316","display_name":"Neural Network World","issn_l":"1210-0552","issn":["1210-0552","2336-4335"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-c","jufo-1","norway-1","poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310314184","host_organization_name":"Czech Technical University in Prague","host_organization_lineage":["https://openalex.org/P4310314184"],"host_organization_lineage_names":["Czech Technical University in Prague"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Network World","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"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/A5104197257","display_name":"Veeman Vivek","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Veeman Vivek","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056337268","display_name":"J. Hemalatha","orcid":"https://orcid.org/0000-0003-0793-4191","observed_orcids":["https://orcid.org/0000-0003-0793-4191"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jeyaprakash Hemalatha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023309375","display_name":"Thamarai Pugazhendhi Latchoumi","orcid":"https://orcid.org/0000-0002-6107-9969","observed_orcids":["https://orcid.org/0000-0002-6107-9969"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thamarai Pugazhendhi Latchoumi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Sekar Mohan","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sekar Mohan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4421,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.53591078,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"33","issue":"5","first_page":"337","last_page":"355"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9731000065803528,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9731000065803528,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.00989999994635582,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.00860000029206276,"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-detection","display_name":"obstacle detection","score":0.8309999704360962},{"id":"https://openalex.org/keywords/railway-track","display_name":"railway track","score":0.7369999885559082},{"id":"https://openalex.org/keywords/infrared-thermography","display_name":"infrared thermography","score":0.9800000190734863},{"id":"https://openalex.org/keywords/train-collision-prevention","display_name":"train collision prevention","score":0.628000020980835},{"id":"https://openalex.org/keywords/railway-safety","display_name":"railway safety","score":0.7799999713897705},{"id":"https://openalex.org/keywords/surf-features","display_name":"SURF features","score":0.7059999704360962},{"id":"https://openalex.org/keywords/faster-r-cnn","display_name":"Faster R-CNN","score":0.7260000109672546},{"id":"https://openalex.org/keywords/object-detection","display_name":"object detection","score":0.7919999957084656},{"id":"https://openalex.org/keywords/yolov5-comparison","display_name":"YOLOv5 comparison","score":0.6570000052452087}],"concepts":[{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.85160893201828},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.7503913640975952},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7055984735488892},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.644390344619751},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6397923231124878},{"id":"https://openalex.org/C1975866","wikidata":"https://www.wikidata.org/wiki/Q171448","display_name":"Level crossing","level":2,"score":0.5521588921546936},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.4646551012992859},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44620177149772644},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4161335825920105},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.39785730838775635},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.33566170930862427},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.32784754037857056},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16417038440704346},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14311/nnw.2023.33.019","doi":"https://doi.org/10.14311/nnw.2023.33.019","is_oa":false,"landing_page_url":"https://doi.org/10.14311/nnw.2023.33.019","pdf_url":null,"source":{"id":"https://openalex.org/S206406316","display_name":"Neural Network World","issn_l":"1210-0552","issn":["1210-0552","2336-4335"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-c","jufo-1","norway-1","poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310314184","host_organization_name":"Czech Technical University in Prague","host_organization_lineage":["https://openalex.org/P4310314184"],"host_organization_lineage_names":["Czech Technical University in Prague"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Network World","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":1.0,"id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4293203850","https://openalex.org/W3124687781","https://openalex.org/W4220920460","https://openalex.org/W974762316","https://openalex.org/W4403052652","https://openalex.org/W4293218406","https://openalex.org/W2913789496","https://openalex.org/W2804810786","https://openalex.org/W3176453289","https://openalex.org/W4386021335"],"abstract_inverted_index":{"To":[0,19],"prevent":[1],"collisions":[2],"between":[3],"trains":[4,12],"and":[5,23,59,97,156,166],"objects":[6],"on":[7,41,119],"the":[8,25,42,68,77,103,112,120,146],"railway":[9,21,121],"line,":[10,122],"rugged":[11],"require":[13],"an":[14,150,162],"intelligent":[15],"rail":[16,128],"protection":[17],"system.":[18],"improve":[20],"safety":[22],"reduce":[24],"number":[26],"of":[27,67,79,102,124,149,152],"accidents,":[28],"studies":[29],"are":[30,106],"underway.":[31],"Machine":[32],"learning":[33],"(ML)":[34],"had":[35],"progressed":[36],"rapidly,":[37],"creating":[38],"new":[39],"perspectives":[40],"subject.":[43],"A":[44],"technique":[45,81],"called":[46],"speed":[47],"up":[48],"robust":[49],"features":[50],"(SURF)":[51],"is":[52],"proposed":[53,104,131],"by":[54],"researchers":[55],"to":[56,111,114,127],"collect":[57],"regionally":[58],"globally":[60],"relevant":[61],"information.":[62],"In":[63],"addition,":[64],"taking":[65],"advantage":[66],"Ohio":[69],"State":[70],"University":[71],"(OSU)":[72],"heat":[73],"walker":[74],"benchmarking":[75],"dataset,":[76],"effectiveness":[78],"this":[80],"was":[82],"examined":[83],"under":[84],"various":[85],"lighting":[86],"scenarios.":[87],"This":[88],"technology":[89],"could":[90],"help":[91],"in":[92,109],"reducing":[93],"train":[94],"accident":[95],"rates":[96],"financial":[98],"costs.":[99],"The":[100,130],"findings":[101],"methodology":[105],"very":[107],"specific,":[108],"addition":[110],"ability":[113],"quickly":[115],"identify":[116],"items":[117],"(obstacles)":[118],"both":[123],"which":[125],"contribute":[126],"security.":[129],"faster":[132],"region":[133],"based":[134],"convolutional":[135],"neural":[136],"network":[137],"(FR-CNN)":[138],"with":[139,161,168],"2D":[140,169],"singular":[141],"spectrum":[142],"analysis":[143,148],"(SSA)":[144],"improves":[145],"performance":[147],"accuracy":[151],"90.2%,":[153],"recall":[154],"95.6%":[155],"precision":[157],"94.6%":[158],"when":[159],"compared":[160],"existing":[163],"system":[164],"YOLOv2":[165],"YOLOv5":[167],"SSA.":[170]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":5}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2023-11-14T00:00:00"}