{"id":"https://openalex.org/W4405444580","doi":"https://doi.org/10.3390/rs16244684","title":"Early Wildfire Smoke Detection Method Based on EDA","display_name":"Early Wildfire Smoke Detection Method Based on EDA","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4405444580","doi":"https://doi.org/10.3390/rs16244684"},"language":"en","primary_location":{"id":"doi:10.3390/rs16244684","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16244684","pdf_url":"https://www.mdpi.com/2072-4292/16/24/4684/pdf?version=1734259409","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/16/24/4684/pdf?version=1734259409","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058880564","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0001-7052-4887"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I4210134523","display_name":"State Forestry and Grassland Administration","ror":"https://ror.org/03f2n3n81","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China","School of Technology, Beijing Forestry University, Beijing 100083, China","State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China","institution_ids":["https://openalex.org/I4210134523"]},{"raw_affiliation_string":"School of Technology, Beijing Forestry University, Beijing 100083, China","institution_ids":["https://openalex.org/I31683504"]},{"raw_affiliation_string":"State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China","institution_ids":["https://openalex.org/I4210134523"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055962708","display_name":"Fengmao Chen","orcid":"https://orcid.org/0000-0003-2412-7607"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I4210134523","display_name":"State Forestry and Grassland Administration","ror":"https://ror.org/03f2n3n81","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Faying Chen","raw_affiliation_strings":["Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China","School of Technology, Beijing Forestry University, Beijing 100083, China","State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China","institution_ids":["https://openalex.org/I4210134523"]},{"raw_affiliation_string":"School of Technology, Beijing Forestry University, Beijing 100083, China","institution_ids":["https://openalex.org/I31683504"]},{"raw_affiliation_string":"State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China","institution_ids":["https://openalex.org/I4210134523"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036253151","display_name":"Changchun Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I4210134523","display_name":"State Forestry and Grassland Administration","ror":"https://ror.org/03f2n3n81","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changchun Zhang","raw_affiliation_strings":["Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China","School of Technology, Beijing Forestry University, Beijing 100083, China","State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China","institution_ids":["https://openalex.org/I4210134523"]},{"raw_affiliation_string":"School of Technology, Beijing Forestry University, Beijing 100083, China","institution_ids":["https://openalex.org/I31683504"]},{"raw_affiliation_string":"State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China","institution_ids":["https://openalex.org/I4210134523"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047750405","display_name":"Y. Wang","orcid":"https://orcid.org/0000-0003-2226-4384"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I4210134523","display_name":"State Forestry and Grassland Administration","ror":"https://ror.org/03f2n3n81","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuan Wang","raw_affiliation_strings":["Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China","School of Technology, Beijing Forestry University, Beijing 100083, China","State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China","institution_ids":["https://openalex.org/I4210134523"]},{"raw_affiliation_string":"School of Technology, Beijing Forestry University, Beijing 100083, China","institution_ids":["https://openalex.org/I31683504"]},{"raw_affiliation_string":"State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China","institution_ids":["https://openalex.org/I4210134523"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089221278","display_name":"Junguo Zhang","orcid":"https://orcid.org/0000-0002-2267-786X"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I4210134523","display_name":"State Forestry and Grassland Administration","ror":"https://ror.org/03f2n3n81","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junguo Zhang","raw_affiliation_strings":["Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China","School of Technology, Beijing Forestry University, Beijing 100083, China","State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China","institution_ids":["https://openalex.org/I4210134523"]},{"raw_affiliation_string":"School of Technology, Beijing Forestry University, Beijing 100083, China","institution_ids":["https://openalex.org/I31683504"]},{"raw_affiliation_string":"State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China","institution_ids":["https://openalex.org/I4210134523"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5047750405"],"corresponding_institution_ids":["https://openalex.org/I31683504","https://openalex.org/I4210134523"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":0.7294,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.72313843,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"16","issue":"24","first_page":"4684","last_page":"4684"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9947999715805054,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9832000136375427,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/smoke","display_name":"Smoke","score":0.7585710287094116},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7427113652229309},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.7260767221450806},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.5336509346961975},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.49024635553359985},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4326988756656647},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.34445419907569885},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.33202311396598816},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17498695850372314},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.13398417830467224},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09195998311042786},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.07681500911712646}],"concepts":[{"id":"https://openalex.org/C58874564","wikidata":"https://www.wikidata.org/wiki/Q130768","display_name":"Smoke","level":2,"score":0.7585710287094116},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7427113652229309},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.7260767221450806},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.5336509346961975},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.49024635553359985},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4326988756656647},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.34445419907569885},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.33202311396598816},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17498695850372314},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.13398417830467224},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09195998311042786},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.07681500911712646},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/rs16244684","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16244684","pdf_url":"https://www.mdpi.com/2072-4292/16/24/4684/pdf?version=1734259409","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f04436a455474de1985d155652d07e03","is_oa":true,"landing_page_url":"https://doaj.org/article/f04436a455474de1985d155652d07e03","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 16, Iss 24, p 4684 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/rs16244684","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16244684","pdf_url":"https://www.mdpi.com/2072-4292/16/24/4684/pdf?version=1734259409","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.5600000023841858,"display_name":"Good health and well-being"}],"awards":[{"id":"https://openalex.org/G5758675384","display_name":null,"funder_award_id":"QNTD20","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G722021413","display_name":null,"funder_award_id":"QNTD202304","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G8951484681","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4405444580.pdf","grobid_xml":"https://content.openalex.org/works/W4405444580.grobid-xml"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2752782242","https://openalex.org/W2919974312","https://openalex.org/W2962793481","https://openalex.org/W2977616508","https://openalex.org/W2982151979","https://openalex.org/W3034552520","https://openalex.org/W3091882159","https://openalex.org/W4224297527","https://openalex.org/W4229439898","https://openalex.org/W4280535258","https://openalex.org/W4285395742","https://openalex.org/W4285802070","https://openalex.org/W4298004560","https://openalex.org/W4318828328","https://openalex.org/W4319598957","https://openalex.org/W4376130750","https://openalex.org/W4376643844","https://openalex.org/W4386076325","https://openalex.org/W4388019121","https://openalex.org/W4388937529","https://openalex.org/W4389859929","https://openalex.org/W4390826350","https://openalex.org/W4390839314","https://openalex.org/W4395003018","https://openalex.org/W4398756379","https://openalex.org/W4399039762","https://openalex.org/W4399361386","https://openalex.org/W4399527364","https://openalex.org/W4400146394","https://openalex.org/W4400526481","https://openalex.org/W4400770846","https://openalex.org/W4400909140","https://openalex.org/W4401329242","https://openalex.org/W4402292228","https://openalex.org/W4402383981","https://openalex.org/W4402393282","https://openalex.org/W4402727701","https://openalex.org/W4402754006","https://openalex.org/W4403484183","https://openalex.org/W4403888521","https://openalex.org/W4404520779","https://openalex.org/W6859444809","https://openalex.org/W6868826046","https://openalex.org/W6875508544","https://openalex.org/W7037153631"],"related_works":["https://openalex.org/W2062399876","https://openalex.org/W2607795551","https://openalex.org/W3155117723","https://openalex.org/W1991429770","https://openalex.org/W1983892167","https://openalex.org/W2281134365","https://openalex.org/W4310746709","https://openalex.org/W4386075645","https://openalex.org/W4385574037","https://openalex.org/W4306309518"],"abstract_inverted_index":{"Early":[0],"wildfire":[1,52,136],"smoke":[2,53,137],"detection":[3,67],"faces":[4],"challenges":[5],"such":[6],"as":[7],"limited":[8],"datasets,":[9],"small":[10],"target":[11],"sizes,":[12],"and":[13,30,49,69,80,100,105,133],"interference":[14],"from":[15],"smoke-like":[16],"objects.":[17],"To":[18],"address":[19],"these":[20],"issues,":[21],"we":[22,37,56],"propose":[23],"a":[24,64,91,117],"novel":[25],"approach":[26],"leveraging":[27],"Efficient":[28],"Channel":[29],"Dilated":[31],"Convolution":[32],"Spatial":[33],"Attention":[34],"(EDA).":[35],"Specifically,":[36],"develop":[38],"an":[39,59,113],"experimental":[40],"dataset,":[41],"Smoke-Exp,":[42],"consisting":[43],"of":[44,96,115,127],"6016":[45],"images,":[46],"including":[47],"real-world":[48],"Cycle-GAN-generated":[50],"synthetic":[51],"images.":[54],"Additionally,":[55],"introduce":[57],"M-YOLO,":[58],"enhanced":[60],"YOLOv5-based":[61],"model":[62],"with":[63],"4\u00d7":[65],"downsampling":[66],"head,":[68],"MEDA-YOLO,":[70],"which":[71],"incorporates":[72],"the":[73,125,128],"EDA":[74],"mechanism":[75],"to":[76],"filter":[77],"irrelevant":[78],"information":[79],"suppress":[81],"interference.":[82],"Experimental":[83],"results":[84,123],"on":[85],"Smoke-Exp":[86],"demonstrate":[87],"that":[88],"M-YOLO":[89],"achieves":[90],"mean":[92],"Average":[93],"Precision":[94],"(mAP)":[95],"96.74%,":[97],"outperforming":[98],"YOLOv5":[99],"Faster":[101],"R-CNN":[102],"by":[103],"1.32%":[104],"3.26%,":[106],"respectively.":[107],"MEDA-YOLO":[108],"further":[109],"improves":[110],"performance,":[111],"achieving":[112],"mAP":[114],"97.58%,":[116],"2.16%":[118],"increase":[119],"over":[120],"YOLOv5.":[121],"These":[122],"highlight":[124],"potential":[126],"proposed":[129],"models":[130],"for":[131],"precise":[132],"real-time":[134],"early":[135],"detection.":[138]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
