{"id":"https://openalex.org/W7043047616","doi":null,"title":"Rural Fire Detection: A close-range approach for classification and localisation","display_name":"Rural Fire Detection: A close-range approach for classification and localisation","publication_year":2024,"publication_date":"2024-10-31","ids":{"openalex":"https://openalex.org/W7043047616"},"language":"en","primary_location":{"id":"pmh:oai:run.unl.pt:10362/175116","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10362/175116","pdf_url":"https://run.unl.pt/bitstreams/f1b7331f-2739-445d-8aad-5feb9893d9d0/download","source":{"id":"https://openalex.org/S4306402433","display_name":"Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"master thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://run.unl.pt/bitstreams/f1b7331f-2739-445d-8aad-5feb9893d9d0/download","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Dias, Filipe de Correia Pedro Mar\u00e7al","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Dias, Filipe de Correia Pedro Mar\u00e7al","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":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0006000000284984708,"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/T10616","display_name":"Smart Agriculture and AI","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/fire-classification","display_name":"fire classification","score":0.75},{"id":"https://openalex.org/keywords/near-infrared-images","display_name":"near-infrared imagery","score":0.8460000157356262},{"id":"https://openalex.org/keywords/object-detection","display_name":"object detection","score":0.75},{"id":"https://openalex.org/keywords/yolov8","display_name":"YOLOv8","score":0.9629999995231628},{"id":"https://openalex.org/keywords/efficientdet","display_name":"EfficientDet","score":0.9959999918937683},{"id":"https://openalex.org/keywords/mobilenet","display_name":"MobileNet","score":0.6079999804496765},{"id":"https://openalex.org/keywords/efficientnet","display_name":"EfficientNet","score":0.9919999837875366},{"id":"https://openalex.org/keywords/maxvit","display_name":"MaxViT","score":0.8389999866485596},{"id":"https://openalex.org/keywords/lightweight-models","display_name":"lightweight models","score":0.4779999852180481},{"id":"https://openalex.org/keywords/fire-management","display_name":"fire management","score":0.46299999952316284}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4763999879360199},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C2775997480","wikidata":"https://www.wikidata.org/wiki/Q586277","display_name":"Degree (music)","level":2,"score":0.31439998745918274},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.313400000333786},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3093999922275543},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2840000092983246},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.26089999079704285},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2574000060558319}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:run.unl.pt:10362/175116","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10362/175116","pdf_url":"https://run.unl.pt/bitstreams/f1b7331f-2739-445d-8aad-5feb9893d9d0/download","source":{"id":"https://openalex.org/S4306402433","display_name":"Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"master thesis"}],"best_oa_location":{"id":"pmh:oai:run.unl.pt:10362/175116","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10362/175116","pdf_url":"https://run.unl.pt/bitstreams/f1b7331f-2739-445d-8aad-5feb9893d9d0/download","source":{"id":"https://openalex.org/S4306402433","display_name":"Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"master thesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7043047616.pdf","grobid_xml":"https://content.openalex.org/works/W7043047616.grobid-xml"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1909366555","https://openalex.org/W2040870580","https://openalex.org/W2102605133","https://openalex.org/W2112796928","https://openalex.org/W2163605009","https://openalex.org/W2402144811","https://openalex.org/W2612445135","https://openalex.org/W2626778328","https://openalex.org/W2807862495","https://openalex.org/W2889985731","https://openalex.org/W2914296386","https://openalex.org/W2936299508","https://openalex.org/W2941454446","https://openalex.org/W2944779197","https://openalex.org/W2946948417","https://openalex.org/W2963294168","https://openalex.org/W2975479989","https://openalex.org/W3012420847","https://openalex.org/W3026536359","https://openalex.org/W3034971973","https://openalex.org/W3041941398","https://openalex.org/W3094502228","https://openalex.org/W3120107013","https://openalex.org/W3132971810","https://openalex.org/W3136931902","https://openalex.org/W3138516171","https://openalex.org/W3145444543","https://openalex.org/W4205172069","https://openalex.org/W4214895504","https://openalex.org/W4283716939","https://openalex.org/W4283808550","https://openalex.org/W4285334023","https://openalex.org/W4296126030","https://openalex.org/W4312349930","https://openalex.org/W4312847199","https://openalex.org/W4313054714","https://openalex.org/W4319297957","https://openalex.org/W4324092370","https://openalex.org/W4376610688","https://openalex.org/W4378464870","https://openalex.org/W4380193920","https://openalex.org/W4385625933","https://openalex.org/W4385812557","https://openalex.org/W4388823657","https://openalex.org/W4394670654","https://openalex.org/W6887816557"],"related_works":["https://openalex.org/W7056050120","https://openalex.org/W143814028","https://openalex.org/W6980921926","https://openalex.org/W6999627467","https://openalex.org/W7011841215","https://openalex.org/W7065201090","https://openalex.org/W7160287774","https://openalex.org/W7009782842","https://openalex.org/W2267934504","https://openalex.org/W64128872"],"abstract_inverted_index":{"Rural":[0],"fires":[1],"present":[2],"a":[3,33,94,118,167,174],"significant":[4],"danger":[5],"to":[6,16,31,106],"both":[7],"human":[8],"lives":[9],"and":[10,20,28,51,67,79,90,100,124,155,164,234,239],"the":[11,58,69,114,136,145,152,159,187,213,225,228,231,243],"environment.":[12],"This":[13,222],"research":[14],"aims":[15],"improve":[17],"fire":[18,36],"management":[19],"firefighting":[21],"strategies":[22],"by":[23,93],"utilizing":[24],"computer":[25],"vision":[26],"(CV)":[27],"deep":[29],"learning":[30],"develop":[32],"close-range":[34],"rural":[35],"detection":[37],"framework.":[38],"The":[39,54,81,110,133,148,200],"focus":[40],"is":[41],"on":[42,64,218],"using":[43],"low":[44],"computational":[45],"cost":[46],"devices":[47],"capable":[48],"of":[49,60,71,117,227],"localizing":[50],"classifying":[52],"fires.":[53],"study":[55],"also":[56],"explores":[57],"impact":[59],"near-infrared":[61],"(NIR)":[62],"images":[63,195],"model":[65,198],"performance":[66],"compares":[68],"effectiveness":[70],"two":[72,85,232],"key":[73],"techniques:":[74],"Convolutional":[75],"Neural":[76],"Networks":[77],"(CNN)":[78],"Transformers.":[80],"proposed":[82],"framework":[83,189,214],"comprises":[84],"object":[86],"detector":[87,137],"models,":[88],"YoloV8":[89],"EfficientDet,":[91],"followed":[92],"classification":[95,146],"module.":[96,109,147],"Two":[97],"CNN-based":[98],"models":[99,103,138,157],"three":[101],"Transformer-based":[102],"were":[104],"used":[105,141],"evaluate":[107],"this":[108,183,209],"input":[111],"data":[112],"for":[113,144,182,208,216],"detectors":[115,233],"consists":[116],"newly":[119],"created":[120],"dataset":[121],"containing":[122],"\"Fire\"":[123],"\"No":[125],"Fire\"":[126],"images,":[127],"compiled":[128],"from":[129,135],"five":[130],"other":[131],"datasets.":[132],"outputs":[134],"are":[139,206],"then":[140],"as":[142],"inputs":[143],"results":[149],"indicate":[150],"that":[151,203],"MobileNet,":[153],"EfficientNet,":[154],"MaxViT":[156],"exhibited":[158],"highest":[160],"performance.":[161],"Specifically,":[162],"mobilenet_v3_small_pt":[163],"efficientnet_v2_s_pt":[165],"achieved":[166],"99%":[168],"F1":[169,176],"score,":[170,177],"while":[171],"maxvit_t_pt":[172],"reached":[173],"98%":[175],"showcasing":[178],"their":[179],"strong":[180],"suitability":[181],"task.":[184],"In":[185],"conclusion,":[186],"developed":[188],"demonstrated":[190],"robust":[191],"performance,":[192],"with":[193],"NIR":[194],"significantly":[196],"enhancing":[197],"accuracy.":[199],"findings":[201],"suggest":[202],"smaller":[204],"networks":[205],"adequate":[207],"specific":[210],"task,":[211],"making":[212],"suitable":[215],"deployment":[217],"low-computation":[219],"resource":[220],"devices.":[221],"adaptability":[223],"enables":[224],"use":[226],"framework,":[229],"including":[230],"one":[235],"classifier,":[236],"in":[237],"microprocessors":[238],"robotic":[240],"systems,":[241],"considering":[242],"overall":[244],"floating-point":[245],"operations":[246],"per":[247],"second":[248],"required.":[249]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2025-10-10T00:00:00"}
