Weather recognition using DenseNet with multihead attention mechanism

In the field of weather image recognition, the recognition of weather categories should not only consider local features, but also needs to consider the global multi-feature synthesis to avoid misjudge. To address this issue, a weather recognition method based on image organic fusion of deep neural network DenseNet and multi-head attention mechanism is proposed. Among them, DenseNet is responsible for capturing local features in weather images, while the attention mechanism is responsible for focusing on the global feature information of the image, so that the organic fusion of local and global features can improve the accuracy of weather recognition. The proposed method can recognize six types of weather—cloudy, haze, rainy, snow, sunny and thunder. The experimental results show that the model using DenseNet alone achieves 85.82% accuracy on the test set, while the accuracy of the model with the fusion of the attention mechanism increases to 87.45%. This suggests that fusing deep neural networks with attention mechanisms is an effective way to recognize weather categories.