基于改进Swin Transformer的森林火灾检测算法-豆柴文库

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基于改进SwinTransformer的森林火灾检测算法
Title:ForestFireDetectionAlgorithmbasedonImprovedSwinTransformer
Introduction:
Forestfiresposeasignificantthreattotheenvironment,biodiversity,andhumanlives.Earlydetectionandpromptresponsearecrucialinmitigatingtheimpactofsuchdisasters.Withtherecentadvancementsincomputervisionanddeeplearning,theapplicationofartificialintelligencetechniquesforforestfiredetectionhasgainedsignificantattention.Inthispaper,weproposeanimprovedSwinTransformer-basedalgorithmforforestfiredetection,aimingtoachievehigheraccuracyandefficiencyinidentifyingandlocalizingfireincidents.
1.Background:
1.1ForestFireDetection:
Forestfiredetectionplaysavitalroleinmitigatingthedamagecausedbywildfires.Earlydetectionallowsfortimelyinterventionandfirefighting.Varioustechniques,includingsatelliteimageryanalysisandground-basedmonitoringsystems,havebeenemployedforforestfiredetection.However,thesetraditionalmethodsoftensufferfromlimitationsintermsofaccuracy,efficiency,andreal-timemonitoringcapabilities.
1.2SwinTransformer:
TheSwinTransformerhasemergedasapowerfulvariantofthetransformermodel,initiallyproposedforcomputervisiontasks.Itimprovesuponthecomputationalefficiencyofthevanillatransformerwhilemaintainingitsabilitytocapturelong-rangedependencies.TheSwinTransformerachievesthisthroughahierarchicalstructureandashiftwindowmechanism,whichdividestheinputimageintopatchesandaggregatesinformationacrossdifferentscales.
2.Methodology:
2.1DatasetPreparation:
Acomprehensivedatasetcontainingimagesofforestswithandwithoutfireincidentsiscollected.Thedatasetisdividedintotraining,validation,andtestingsets,ensuringanadequaterepresentationoffirescenariosandcapturingdifferentenvironmentalconditions.
2.2Preprocessing:
Theimagesarepreprocessedtoenhancethekeyfeaturesrelatedtofire,suchascolor,texture,andshape.Techniqueslikecontrastenhancement,noisereduction,andcolorcorrectionareappliedtoimprovetheoverallimagequality.
2.3SwinTransformerArchitecture:
TheSwinTransformerarchitectureisadaptedforforestfiredetectio