An Intelligent Classification Method for Digital Images Based on an Improved U-Net Convolutional Neural Network
Environmental noise and interference may have a negative impact on the performance of intelligent digital image classification methods,leading to slow classification speed.To this end,a digital image intelligent classification method based on an improved U-Net convolutional neural network is studied.Improve the quality of digital images through denoising,enhance-ment,and standardization processing;Utilizing an improved U-Net convolutional neural net-work to enhance the dimensionality of preprocessed images,enabling the network to learn richer image information;Using segmentation algorithms to divide the image into multiple regions,u-sing precise keypoint localization techniques to accurately identify key feature points in the im-age;Compare the similarity between the image to be classified and the image of a known catego-ry to achieve intelligent classification.The experimental results show that compared with tradi-tional classification methods,the new method has faster classification speed and higher practical application value.
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