Classifying Weather Images using Deep Neural Networks for Large Scale Datasets

Classifying weather from outdoor images helps prevent road accidents, schedule outdoor activities, and improve the reliability of vehicle assistant driving and outdoor video surveillance systems. Weather classification has applications in various fields such as agriculture, aquaculture, transportation, tourism, etc. Earlier, expensive sensors and huge manpower were used for weather classification making it very tedious and time-consuming. Automating the task of classifying weather conditions from images will save a huge time and resources. In this paper, a framework based on the transfer learning technique has been proposed for classifying the weather images with the features learned from pre-trained deep CNN models in much lesser time. Further, the size of the training data affects the efficiency of the model. The larger amount of high-quality data often leads to more accurate results. Hence, we have implemented the proposed framework using the spark platform making it scalable for big datasets. Extensive experiments have been performed on weather image dataset and the results proved that the proposed framework is reliable. From the results, it can be concluded that weather classification with the InceptionV3 model and Logistic Regression classifier yields the best results with a maximum accuracy of 97.77%.