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Component detection of electronic ceramic composite in battery based on CNN convolution neural network

When the common methods are used to detect the composition of composite materials, the detection time is long and the accuracy is low. Therefore, a method based on CNN convolution neural network is proposed to detect the composition of electronic ceramic composite materials in batteries. The near-infrared image of the lossless area of the electronic ceramic composite is collected by locating the lossless area of the electronic ceramic composite and keeping the battery in a high temperature state. Through color correction and geometric correction, the near-infrared image is preprocessed, and the image features are extracted. Then the image features are input into CNN convolution neural network to iteratively train and learn the image features and judge the composition of the electronic ceramic composite. The experimental results show that the design method improves the detection accuracy of composite material composition and shortens the detection time.