{"id":"https://openalex.org/W7225662000","doi":"https://doi.org/10.1088/2631-8695/aebf50","title":"CHG-YOLO: A Lightweight Forest Fire Detection Framework","display_name":"CHG-YOLO: A Lightweight Forest Fire Detection Framework","publication_year":2026,"publication_date":"2026-11-09","ids":{"openalex":"https://openalex.org/W7225662000","doi":"https://doi.org/10.1088/2631-8695/aebf50"},"language":"en","primary_location":{"id":"doi:10.1088/2631-8695/aebf50","doi":"https://doi.org/10.1088/2631-8695/aebf50","is_oa":false,"landing_page_url":"https://doi.org/10.1088/2631-8695/aebf50","pdf_url":null,"source":{"id":"https://openalex.org/S4210221144","display_name":"Engineering Research Express","issn_l":"2631-8695","issn":["2631-8695"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","norway-1","poland-20","russia-white-list-3"],"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Research Express","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/A5161303939","display_name":"zijie zhang","orcid":"https://orcid.org/0009-0000-6425-6249","observed_orcids":["https://orcid.org/0009-0000-6425-6249"]},"institutions":[{"id":"https://openalex.org/I38706770","display_name":"Guilin University of Technology","ror":"https://ror.org/03z391397","country_code":"CN","type":"education","lineage":["https://openalex.org/I38706770"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"zijie zhang","raw_affiliation_strings":["Guilin University of Technology"],"raw_orcid":"https://orcid.org/0009-0000-6425-6249","affiliations":[{"raw_affiliation_string":"Guilin University of Technology","institution_ids":["https://openalex.org/I38706770"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161316787","display_name":"Zhaomiao Zheng","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhaomiao Zheng","raw_affiliation_strings":["Guangxi Great Wall Computer Technology Co. Ltd"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangxi Great Wall Computer Technology Co. Ltd","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161305589","display_name":"Junkai Wang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405254957","display_name":"Guilin Institute of Information Technology","ror":"https://ror.org/02ed17502","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405254957"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junkai Wang","raw_affiliation_strings":["Guilin Institute of Information Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guilin Institute of Information Technology","institution_ids":["https://openalex.org/I4405254957"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020353409","display_name":"Yuxiao Jiang","orcid":"https://orcid.org/0000-0003-0387-5665","observed_orcids":["https://orcid.org/0000-0003-0387-5665"]},"institutions":[{"id":"https://openalex.org/I3129996945","display_name":"Guilin University of Aerospace Technology","ror":"https://ror.org/00h1gc758","country_code":"CN","type":"education","lineage":["https://openalex.org/I3129996945"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxiao Jiang","raw_affiliation_strings":["Guilin University of Aerospace Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guilin University of Aerospace Technology","institution_ids":["https://openalex.org/I3129996945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045323971","display_name":"Lubin Wang","orcid":"https://orcid.org/0009-0006-4513-9403","observed_orcids":["https://orcid.org/0009-0006-4513-9403"]},"institutions":[{"id":"https://openalex.org/I4405254957","display_name":"Guilin Institute of Information Technology","ror":"https://ror.org/02ed17502","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405254957"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"\u9646\u658c \u738b","raw_affiliation_strings":["Guilin Institute of Information Technology"],"raw_orcid":"https://orcid.org/0009-0006-4513-9403","affiliations":[{"raw_affiliation_string":"Guilin Institute of Information Technology","institution_ids":["https://openalex.org/I4405254957"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126498198","display_name":"Peng Huang","orcid":"https://orcid.org/0009-0005-2634-7912","observed_orcids":["https://orcid.org/0009-0005-2634-7912"]},"institutions":[{"id":"https://openalex.org/I38706770","display_name":"Guilin University of Technology","ror":"https://ror.org/03z391397","country_code":"CN","type":"education","lineage":["https://openalex.org/I38706770"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Huang","raw_affiliation_strings":["Guilin University of Technology"],"raw_orcid":"https://orcid.org/0009-0005-2634-7912","affiliations":[{"raw_affiliation_string":"Guilin University of Technology","institution_ids":["https://openalex.org/I38706770"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100717173","display_name":"Qiang Yu","orcid":"https://orcid.org/0000-0001-9856-0548","observed_orcids":["https://orcid.org/0000-0001-9856-0548"]},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210115570","display_name":"National Space Science Center","ror":"https://ror.org/02nnjtm50","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210115570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Yu","raw_affiliation_strings":["National Space Science Center, Chinese Academy of Sciences"],"raw_orcid":"https://orcid.org/0000-0001-9856-0548","affiliations":[{"raw_affiliation_string":"National Space Science Center, Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210115570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2400,"currency":"USD","value_usd":2400},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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":0.9970999956130981,"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":0.9970999956130981,"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":0.0027000000700354576,"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":[],"concepts":[{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6845999956130981},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.684499979019165},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.6342999935150146},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6234999895095825},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5878999829292297},{"id":"https://openalex.org/C2780836893","wikidata":"https://www.wikidata.org/wiki/Q19922674","display_name":"Fire detection","level":2,"score":0.5004000067710876},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4722999930381775},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42489999532699585},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.41179999709129333},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.38190001249313354},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3772999942302704},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.35929998755455017},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35670000314712524},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.35429999232292175},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3474999964237213},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.34630000591278076},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.34389999508857727},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3041999936103821},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.29440000653266907},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C58874564","wikidata":"https://www.wikidata.org/wiki/Q130768","display_name":"Smoke","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1088/2631-8695/aebf50","doi":"https://doi.org/10.1088/2631-8695/aebf50","is_oa":false,"landing_page_url":"https://doi.org/10.1088/2631-8695/aebf50","pdf_url":null,"source":{"id":"https://openalex.org/S4210221144","display_name":"Engineering Research Express","issn_l":"2631-8695","issn":["2631-8695"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","norway-1","poland-20","russia-white-list-3"],"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Research Express","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Abstract:":[1],"To":[2],"address":[3],"the":[4,21,35,63,86,96,119,124,142,148,184,190,199,221,259,275,279],"challenges":[5],"of":[6,10,17,23,37,126,151,192,202,232,236,240,245,254,267,278],"substantial":[7],"scale":[8],"variations":[9],"flame":[11,153,206],"and":[12,20,45,76,100,105,112,129,154,157,173,197,208,215,242,263,296,312],"smoke":[13,155,209],"targets,":[14,19],"insufficient":[15],"discriminability":[16,150],"weak-feature":[18],"susceptibility":[22],"fine-grained":[24,77,179,193,203],"information":[25,78,132,180,194],"to":[26,94,122],"loss":[27,133,191],"in":[28,41,163,205],"complex":[29,159],"forest":[30,53,164,223,304],"environments,":[31],"as":[32,34,309],"well":[33],"difficulty":[36],"existing":[38],"detection":[39,43,55,292],"models":[40],"balancing":[42],"accuracy":[44],"lightweight":[46,52,294],"design,":[47,295],"this":[48],"study":[49],"proposes":[50],"a":[51,174,230,234,252,288,300],"fire":[54,224,305,314],"framework,":[56],"termed":[57],"CHG-YOLO.":[58],"The":[59,80,136,178],"framework":[60,281],"collaboratively":[61],"optimizes":[62],"network":[64],"architecture":[65],"along":[66],"three":[67],"dimensions,":[68],"namely":[69],"global-local":[70,81],"feature":[71,74,82,131,138,149,213],"perception,":[72],"weak-target":[73,137],"enhancement,":[75,168],"preservation.":[79],"perception":[83,104],"layer":[84,140,182],"incorporates":[85,141,183],"proposed":[87,143,185,280],"C3k2_RBC":[88],"module,":[89,145,187],"which":[90,146,188],"leverages":[91],"large-kernel":[92],"reparameterization":[93],"expand":[95],"effective":[97],"receptive":[98],"field":[99],"enhance":[101],"global":[102],"contextual":[103],"local":[106],"detail":[107],"extraction":[108,214],"capabilities":[109],"for":[110,303],"flames":[111],"smoke.":[113],"It":[114],"is":[115],"further":[116,273],"combined":[117],"with":[118,247,258],"ADown":[120],"module":[121],"reduce":[123],"number":[125],"model":[127],"parameters":[128],"mitigate":[130],"during":[134,195],"downsampling.":[135],"enhancement":[139],"HyperACE_Fire":[144],"enhances":[147,198],"weak":[152],"targets":[156],"suppresses":[158],"background":[160],"noise":[161],"interference":[162],"environments":[165],"through":[166,211],"attention":[167],"multi-scale":[169],"spatial":[170],"context":[171],"modeling,":[172],"feature-adaptive":[175],"fusion":[176],"mechanism.":[177],"preservation":[181],"GSConvE":[186],"reduces":[189],"downsampling":[196],"representation":[200],"capability":[201],"features":[204],"textures":[207],"edges":[210],"dual-path":[212],"channel":[216],"reorganization.":[217],"Experimental":[218],"results":[219],"on":[220],"self-built":[222],"dataset":[225],"show":[226],"that":[227],"CHG-YOLO":[228],"achieves":[229,287],"precision":[231],"83.2%,":[233],"recall":[235],"77.1%,":[237],"an":[238,243,264],"mAP50":[239],"84.2%,":[241],"mAP50-95":[244],"64.8%,":[246],"2.2":[248],"M":[249],"parameters,":[250],"representing":[251],"reduction":[253],"approximately":[255],"8.3%":[256],"compared":[257],"YOLOv13":[260],"baseline":[261],"model,":[262],"inference":[265,297],"speed":[266],"375.7":[268],"FPS.":[269],"Additionally,":[270],"cross-dataset":[271],"testing":[272],"validates":[274],"generalization":[276],"stability":[277],"across":[282],"different":[283],"scenarios.":[284],"Meanwhile,":[285],"it":[286],"favorable":[289],"balance":[290],"among":[291],"accuracy,":[293],"efficiency,":[298],"providing":[299],"useful":[301],"reference":[302],"monitoring":[306],"applications,":[307],"such":[308],"UAV":[310],"inspection":[311],"edge-based":[313],"monitoring.":[315]},"counts_by_year":[],"updated_date":"2026-11-10T13:44:29.811591","created_date":"2026-11-10T00:00:00"}