Real-Time Weather Classification using Transfer Learning for Enhanced Safety and Decision-Making

Weather conditions are critical in various aspects of daily life, particularly in transportation safety, where adverse weather can lead to accidents and disruptions. Traditional weather monitoring systems often rely on expensive sensors or human observation, which are prone to errors and may not provide real-time data necessary for immediate decision-making. To address this, an automated weather classification system is needed to identify weather conditions from single images accurately. This research proposes a deep learning approach leveraging transfer learning with the pre-trained InceptionV3 model to classify five distinct weather conditions: cloudy, foggy, rainy, sunny, and sunrise. A dataset was used to train the model of labelled weather images and fine-tuned through transfer learning to improve performance. The results show that the proposed transfer learning model achieved a training accuracy of 99.67% and a testing accuracy of 91.83%, outperforming other models. Additionally, the transfer learning model demonstrated superior generalization, with significantly lower testing loss than CNN.