{"id":"https://openalex.org/W3012452689","doi":"https://doi.org/10.1109/la-cci47412.2019.9037059","title":"Lightweight and efficient octave convolutional neural network for fire recognition","display_name":"Lightweight and efficient octave convolutional neural network for fire recognition","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3012452689","doi":"https://doi.org/10.1109/la-cci47412.2019.9037059","mag":"3012452689"},"language":"en","primary_location":{"id":"doi:10.1109/la-cci47412.2019.9037059","is_oa":false,"landing_page_url":"https://doi.org/10.1109/la-cci47412.2019.9037059","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://figshare.com/articles/conference_contribution/Lightweight_and_efficient_octave_convolutional_neural_network_for_fire_recognition/20708698","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055996958","display_name":"Angel Ayala","orcid":"https://orcid.org/0000-0003-0732-7017"},"institutions":[{"id":"https://openalex.org/I71437568","display_name":"Universidade de Pernambuco","ror":"https://ror.org/00gtcbp88","country_code":"BR","type":"education","lineage":["https://openalex.org/I71437568"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Angel Ayala","raw_affiliation_strings":["Escola Polit\u00e9cnica de Pernambuco, Universidade de Pernambuco, Recife, Brasil"],"affiliations":[{"raw_affiliation_string":"Escola Polit\u00e9cnica de Pernambuco, Universidade de Pernambuco, Recife, Brasil","institution_ids":["https://openalex.org/I71437568"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023998002","display_name":"Estanislau Lima","orcid":"https://orcid.org/0000-0002-8373-5357"},"institutions":[{"id":"https://openalex.org/I71437568","display_name":"Universidade de Pernambuco","ror":"https://ror.org/00gtcbp88","country_code":"BR","type":"education","lineage":["https://openalex.org/I71437568"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Estanislau Lima","raw_affiliation_strings":["Escola Polit\u00e9cnica de Pernambuco, Universidade de Pernambuco, Recife, Brasil"],"affiliations":[{"raw_affiliation_string":"Escola Polit\u00e9cnica de Pernambuco, Universidade de Pernambuco, Recife, Brasil","institution_ids":["https://openalex.org/I71437568"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059739611","display_name":"Bruno Fernandes","orcid":"https://orcid.org/0000-0002-6001-3925"},"institutions":[{"id":"https://openalex.org/I71437568","display_name":"Universidade de Pernambuco","ror":"https://ror.org/00gtcbp88","country_code":"BR","type":"education","lineage":["https://openalex.org/I71437568"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Bruno Fernandes","raw_affiliation_strings":["Escola Polit\u00e9cnica de Pernambuco, Universidade de Pernambuco, Recife, Brasil"],"affiliations":[{"raw_affiliation_string":"Escola Polit\u00e9cnica de Pernambuco, Universidade de Pernambuco, Recife, Brasil","institution_ids":["https://openalex.org/I71437568"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049490188","display_name":"Byron Leite Dantas Bezerra","orcid":"https://orcid.org/0000-0002-8327-9734"},"institutions":[{"id":"https://openalex.org/I71437568","display_name":"Universidade de Pernambuco","ror":"https://ror.org/00gtcbp88","country_code":"BR","type":"education","lineage":["https://openalex.org/I71437568"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Byron L. D. Bezerra","raw_affiliation_strings":["Escola Polit\u00e9cnica de Pernambuco, Universidade de Pernambuco, Recife, Brasil"],"affiliations":[{"raw_affiliation_string":"Escola Polit\u00e9cnica de Pernambuco, Universidade de Pernambuco, Recife, Brasil","institution_ids":["https://openalex.org/I71437568"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044098673","display_name":"Francisco Cruz","orcid":"https://orcid.org/0000-0002-1131-3382"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Francisco Cruz","raw_affiliation_strings":["School of Information Technology, Deakin University, Geelong, Australia"],"affiliations":[{"raw_affiliation_string":"School of Information Technology, Deakin University, Geelong, Australia","institution_ids":["https://openalex.org/I149704539"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5055996958"],"corresponding_institution_ids":["https://openalex.org/I71437568"],"apc_list":null,"apc_paid":null,"fwci":1.5524,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.82552445,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"abs 1512 3385","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":1.0,"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":1.0,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9972000122070312,"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/T12222","display_name":"IoT-based Smart Home Systems","score":0.9790999889373779,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/computer-science","display_name":"Computer science","score":0.8393986225128174},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8302046060562134},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6641039252281189},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6173155307769775},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.46390482783317566},{"id":"https://openalex.org/keywords/octave","display_name":"Octave (electronics)","score":0.4637485146522522},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.4601697325706482},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4577331840991974},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4421570599079132},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4259442687034607},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4236452579498291},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3993217945098877},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3768202066421509},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07596710324287415}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8393986225128174},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8302046060562134},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6641039252281189},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6173155307769775},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.46390482783317566},{"id":"https://openalex.org/C85841341","wikidata":"https://www.wikidata.org/wiki/Q1135984","display_name":"Octave (electronics)","level":2,"score":0.4637485146522522},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.4601697325706482},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4577331840991974},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4421570599079132},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4259442687034607},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4236452579498291},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3993217945098877},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3768202066421509},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07596710324287415},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/la-cci47412.2019.9037059","is_oa":false,"landing_page_url":"https://doi.org/10.1109/la-cci47412.2019.9037059","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","raw_type":"proceedings-article"},{"id":"pmh:oai:dro.deakin.edu.au:DU:30136323","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401102","display_name":"Own your potential (DEAKIN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I149704539","host_organization_name":"Deakin University","host_organization_lineage":["https://openalex.org/I149704539"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"},{"id":"pmh:oai:figshare.com:article/20708698","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Lightweight_and_efficient_octave_convolutional_neural_network_for_fire_recognition/20708698","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"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":"","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/20708698","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Lightweight_and_efficient_octave_convolutional_neural_network_for_fire_recognition/20708698","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"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":"","raw_type":"Text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1597822402","https://openalex.org/W1686810756","https://openalex.org/W1909366555","https://openalex.org/W2027645937","https://openalex.org/W2078964886","https://openalex.org/W2087593939","https://openalex.org/W2088931902","https://openalex.org/W2147985963","https://openalex.org/W2152615781","https://openalex.org/W2163614394","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2282689446","https://openalex.org/W2530898649","https://openalex.org/W2618530766","https://openalex.org/W2741119099","https://openalex.org/W2761592246","https://openalex.org/W2807860345","https://openalex.org/W2807862495","https://openalex.org/W2901982721","https://openalex.org/W2919115771","https://openalex.org/W2947516080","https://openalex.org/W2962835968","https://openalex.org/W2988396473","https://openalex.org/W3105270602","https://openalex.org/W3152930111","https://openalex.org/W6637373629","https://openalex.org/W6687483927","https://openalex.org/W6752163004","https://openalex.org/W6763710828","https://openalex.org/W6793606133"],"related_works":["https://openalex.org/W9190101","https://openalex.org/W1383942","https://openalex.org/W14516383","https://openalex.org/W7303821","https://openalex.org/W9333608","https://openalex.org/W5939791","https://openalex.org/W7329816","https://openalex.org/W12793662","https://openalex.org/W10820300","https://openalex.org/W12336802"],"abstract_inverted_index":{"Fire":[0],"recognition":[1,108],"from":[2],"visual":[3,110],"scenes":[4],"is":[5],"a":[6,57,98],"demanding":[7],"task":[8],"due":[9],"to":[10,32,151],"the":[11],"high":[12],"variance":[13],"of":[14,45,49,60],"color":[15],"and":[16,47,56,81,100,120,130],"texture.":[17],"In":[18,123],"recent":[19],"years,":[20],"several":[21],"fire-recognition":[22],"approaches":[23],"based":[24],"on":[25,116],"deep":[26,38,64],"learning":[27],"methods":[28],"have":[29],"been":[30],"proposed":[31,137],"overcome":[33],"this":[34,91,94],"problem.":[35],"However,":[36],"building":[37],"convolutional":[39,103],"neural":[40,104],"networks":[41,65],"usually":[42],"involves":[43],"hundreds":[44],"layers":[46,129],"thousands":[48],"channels,":[50],"thus":[51],"requiring":[52],"excessive":[53],"computational":[54],"cost,":[55],"considerable":[58],"amount":[59],"data.":[61],"Therefore,":[62],"applying":[63],"in":[66,79,93,109,133,149],"real-world":[67],"scenarios":[68],"remains":[69],"an":[70],"open":[71],"challenge,":[72,92],"especially":[73],"when":[74],"using":[75],"devices":[76],"with":[77,135],"limitations":[78],"hardware":[80],"computing":[82],"power,":[83],"e.g.,":[84],"robots":[85],"or":[86],"mobile":[87],"devices.":[88],"To":[89],"address":[90],"paper,":[95],"we":[96],"propose":[97],"lightweight":[99],"efficient":[101],"octave":[102],"network":[105],"for":[106,154],"fire":[107],"scenes.":[111],"Extensive":[112],"experiments":[113],"are":[114],"conducted":[115],"FireSense,":[117],"CairFire,":[118],"FireNet,":[119],"FiSmo":[121],"datasets.":[122,157],"overall,":[124],"our":[125,143],"architecture":[126],"comprises":[127],"fewer":[128,131],"parameters":[132],"comparison":[134,150],"previously":[136],"architectures.":[138],"Experimental":[139],"results":[140],"show":[141],"that":[142],"model":[144],"achieves":[145],"higher":[146],"accuracy":[147],"recognition,":[148],"state-of-the-art":[152],"methods,":[153],"all":[155],"tested":[156]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}