Research on Smoke Segmentation Model Based on Frequency Transformer CNN Coupling
This article delves into the study of smoke segmentation methods in real-time monitoring of wildfires near transmission lines,which helps to accurately extract information such as smoke volume,diffusion direction,and source from images.This is of great significance for developing emergency plans.To this end,a dual branch segmentation model called CFT-Net was proposed in the study.This model combines the frequency Transformer branch with the CNN branch to optimize the representation of global and local features.In addition,this article also designs a hybrid self attention fusion module(HSAM)to efficiently fuse information from the frequency Transformer branch and the CNN branch.Research has shown that the performance of this algorithm is superior to other mainstream segmentation methods.
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