{"id":"https://openalex.org/W4301748379","doi":"https://doi.org/10.21428/594757db.7c1cd4e1","title":"Detecting Flashover in a Room Fire based on the Sequence of Thermal Infrared Images using Convolutional Neural Networks","display_name":"Detecting Flashover in a Room Fire based on the Sequence of Thermal Infrared Images using Convolutional Neural Networks","publication_year":2022,"publication_date":"2022-05-27","ids":{"openalex":"https://openalex.org/W4301748379","doi":"https://doi.org/10.21428/594757db.7c1cd4e1"},"language":"en","primary_location":{"id":"doi:10.21428/594757db.7c1cd4e1","is_oa":true,"landing_page_url":"https://doi.org/10.21428/594757db.7c1cd4e1","pdf_url":"https://caiac.pubpub.org/pub/dr53cmld/download/pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Canadian Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://caiac.pubpub.org/pub/dr53cmld/download/pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084961617","display_name":"M. Hamed Mozaffari","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"M. Hamed Mozaffari","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101957410","display_name":"Yuchuan Li","orcid":"https://orcid.org/0000-0002-4909-2853"},"institutions":[{"id":"https://openalex.org/I4210159778","display_name":"National Research Council Canada","ror":"https://ror.org/04mte1k06","country_code":"CA","type":"government","lineage":["https://openalex.org/I4210159778"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yuchuan Li","raw_affiliation_strings":["Safety Unit, Construction Research Centre, National Research Council Canada, Ottawa, Ontario, Canada, K1A 0R6"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Safety Unit, Construction Research Centre, National Research Council Canada, Ottawa, Ontario, Canada, K1A 0R6","institution_ids":["https://openalex.org/I4210159778"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076585994","display_name":"Yoon Ho Ko","orcid":"https://orcid.org/0000-0002-2506-3740"},"institutions":[{"id":"https://openalex.org/I4210159778","display_name":"National Research Council Canada","ror":"https://ror.org/04mte1k06","country_code":"CA","type":"government","lineage":["https://openalex.org/I4210159778"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yoon Ko","raw_affiliation_strings":["Safety Unit, Construction Research Centre, National Research Council Canada, Ottawa, Ontario, Canada, K1A 0R6"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Safety Unit, Construction Research Centre, National Research Council Canada, Ottawa, Ontario, Canada, K1A 0R6","institution_ids":["https://openalex.org/I4210159778"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3985,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.79307087,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T11317","display_name":"Fire dynamics and safety research","score":0.978600025177002,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/arc-flash","display_name":"Arc flash","score":0.7876851558685303},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6196686029434204},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5262317061424255},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5261639952659607},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46895450353622437},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3865404725074768},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.37297701835632324},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.3657517433166504},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.3515801727771759},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.09234499931335449},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.08068472146987915}],"concepts":[{"id":"https://openalex.org/C200769187","wikidata":"https://www.wikidata.org/wiki/Q2360656","display_name":"Arc flash","level":3,"score":0.7876851558685303},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6196686029434204},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5262317061424255},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5261639952659607},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46895450353622437},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3865404725074768},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.37297701835632324},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3657517433166504},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3515801727771759},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.09234499931335449},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.08068472146987915}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21428/594757db.7c1cd4e1","is_oa":true,"landing_page_url":"https://doi.org/10.21428/594757db.7c1cd4e1","pdf_url":"https://caiac.pubpub.org/pub/dr53cmld/download/pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Canadian Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.21428/594757db.7c1cd4e1","is_oa":true,"landing_page_url":"https://doi.org/10.21428/594757db.7c1cd4e1","pdf_url":"https://caiac.pubpub.org/pub/dr53cmld/download/pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Canadian Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4301748379.pdf","grobid_xml":"https://content.openalex.org/works/W4301748379.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2789876780","https://openalex.org/W2991263362","https://openalex.org/W3092942046","https://openalex.org/W3159535944","https://openalex.org/W3204711149","https://openalex.org/W6600137863","https://openalex.org/W6610002722","https://openalex.org/W6628070019","https://openalex.org/W6676297131","https://openalex.org/W6687483927","https://openalex.org/W6748314208","https://openalex.org/W6823248155","https://openalex.org/W6989086347"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W4312417841","https://openalex.org/W4321369474","https://openalex.org/W2731899572","https://openalex.org/W3133861977","https://openalex.org/W4200173597","https://openalex.org/W3116150086","https://openalex.org/W2999805992","https://openalex.org/W4291897433","https://openalex.org/W3011074480"],"abstract_inverted_index":{"Flashover":[0,131],"phenomena":[1],"accompanying":[2],"rapid":[3],"fire":[4,16,58,70,75,95,141],"propagation":[5],"in":[6,18,59,115,166,218,245,250],"a":[7,15,53,230,238],"room":[8],"occur":[9],"when":[10],"the":[11,19,28,43,127,151,187],"hot":[12],"smoke":[13],"from":[14,196],"accumulates":[17],"room's":[20],"upper":[21],"part.":[22],"This":[23],"phenomenon":[24],"presents":[25],"one":[26],"of":[27,45,57,86,113,119,129,138,153,189],"most":[29],"frightening":[30],"and":[31,41,69,111,125,156,173,183],"challenging":[32],"situations":[33],"for":[34,247],"firefighters.":[35,107],"A":[36],"typical":[37],"approach":[38,214,240],"to":[39,48,51,82,104,122,193],"mitigate":[40],"prevent":[42],"impact":[44],"flashover":[46,88,114,195,217,229,248],"is":[47,90,133,237],"train":[49],"firefighters":[50],"monitor":[52],"few":[54,231],"common":[55],"indicators":[56,79],"pre-flashover":[60,78],"time,":[61],"such":[62],"as":[63],"moving":[64],"dark":[65],"smoke,":[66],"high":[67,223],"heat,":[68],"rollover.":[71],"In":[72],"actual":[73],"compartment":[74],"events,":[76],"these":[77,157],"are":[80,99,118,148],"hard":[81],"recognize.":[83],"Furthermore,":[84],"determination":[85],"exact":[87],"time":[89,117],"difficult":[91],"by":[92,106,140,200],"just":[93],"observing":[94],"activities":[96],"while":[97],"there":[98],"other":[100],"vital":[101],"rescue":[102],"duties":[103],"do":[105],"Hence,":[108],"automatic":[109],"detection":[110],"prediction":[112,132,249],"real":[116,251],"paramount":[120],"importance":[121],"save":[123],"lives":[124],"reduce":[126],"cost":[128],"damages.":[130],"still":[134],"an":[135],"open":[136],"area":[137,152],"research":[139],"safety":[142],"experts.":[143],"Deep":[144],"convolutional":[145],"neural":[146],"networks":[147],"currently":[149],"dominating":[150],"computer":[154],"vision,":[155],"state-of-the-art":[158],"deep":[159,190],"learning":[160,191],"models":[161],"have":[162],"been":[163],"successfully":[164],"used":[165,244],"various":[167],"applications,":[168],"including":[169],"object":[170],"detection,":[171],"localization,":[172],"segmentation.":[174],"Unlike":[175],"previous":[176],"studies":[177],"that":[178,209,241],"use":[179],"RGB":[180],"images,":[181],"sensors,":[182],"gauges,":[184],"we":[185],"utilized":[186],"power":[188],"techniques":[192],"detect":[194,216,228],"image":[197],"sequences":[198],"captured":[199],"thermal":[201],"infrared":[202],"(IR)":[203],"cameras.":[204],"Our":[205,235],"experimental":[206],"results":[207],"indicate":[208],"not":[210],"only":[211],"our":[212],"proposed":[213],"can":[215,227,242],"IR":[219],"video":[220],"data":[221],"with":[222],"precision,":[224],"but":[225],"it":[226],"frames":[232],"before":[233],"happening.":[234],"technique":[236],"promising":[239],"be":[243],"future":[246],"time.":[252]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
