Blurred image recognition based on GLCM

With the development of artificial intelligence, the fault detection of substations has changed from manual to artificial intelligence detection. The higher the image quality, the better the recognition accuracy of the model, but when the inspection image is acquired, transmitted and saved, it is usually affected by bad weather, relative motion, imaging equipment shaking and other factors, which makes the acquired image blurry. Therefore, a fuzzy image screening model is constructed to screen and remove blurred images. In this paper, the grayscale co-occurrence matrix is used to extract the characteristics of the texture feature information of the image, extract the four feature values of the image, take the feature values as the input of the MLP neural network, and use the TID2013 dataset for the training dataset, and finally realize the quality scoring and screening of the blurred image.