Weather Recognition Based on Still Images Using Deep Learning Neural Network with Resnet-15

The recognition of weather condition from still images is quite challenging due to weather diversity and lack of distinct characteristics that exists in many weather conditions. Some researchers have used the K-nearest neighbor method to recognise a specific extract of a weather condition, to test the efficiency of the recognition task. Other works attempted to resolve this problem viewed weather recognition as a single identifier task. In order to enhance the accuracy of recognising weather conditions, this research uses the approach of convolutional layers of Resnet-15 model to extract the essential features of an image. Thereafter, uses the fully connected layers and the softmax classifier to recognise and classify the images, a small size dataset of images from diverse scenes called dataset-2, is used. And Resnet-15 model is used for the testing and training on the datadet-2. The experiments of the proposed approach have been able to correctly recognise the weather conditions of the images, with a better accuracy, speed and reduction in the model size of the network.