Weather Classification Based On Convolutional Neural Networks

Identification and forecast of weather conditions are important for transportation safety, environment, meteorology. Under the background of Artificial intelligence, the methods of weather conditions recognition based on deep learning can solve the problems of traditional weather conditions recognition. In addition, it can also realize the real-time judgment of weather recognition. Convolutional Neural Network (CNN) is an important network structure in deep learning. By introducing convolution layer, pooling layer, and deeper network structure, CNN perceives higher semantic features and enhances the image classification effect. In view of the weather conditions of visual images (sunny, foggy, rainy, snowy) which are difficult to be identified by the traditional weather recognition methods. In this paper, we proposed a weather recognition framework based on the convolution neural network architecture whose accuracy is improved by 5.06% compared with traditional methods.