Image processing and feature extraction technology can effectively solve the problems in using sensors to detect fires.Therefore,the application method of fire image feature extraction technology combined with Darknet-53 is studied.By collecting a large number of fire images and directly calculating the pixel scale,the fire motion targets in the image data are determined.Under the continu-ous frame sequence changes of fire images,decompose representative features in the fire images,divide them according to color features and dynamic features,and distinguish and classify the specific feature vectors of the fire.Select Darknet-53 as the feature extraction mod-el,train the preprocessed images within the model,and use Darknet-53's deep neural network propagation mode to establish an objective matrix for color and dynamic fire feature vectors,achieving the extraction of different target features from fire images.Using fire images from different scenarios as test data for verification,it can be seen that the method studied can achieve complete segmentation of fire fea-tures and accurately extract fire feature data values from continuous frame images,which has high application value.
fire imagesfeature extractiondeep learning neural networkdisaster analysisdata processing
维普
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