Extreme Weather Recognition Using Convolutional Neural Networks

Extreme weather always brings potential risk to driving, which leads to people's life and property being put into great dangers. Therefore, the automatic recognition of extreme weather plays an important role in the application of the highway traffic condition warning, automobile auxiliary driving, climate analysis and so on. Generally, multiple sensors are adopted in traditional methods of automatic extreme weather recognition with artificial participation and low accuracy. A new extreme weather recognition method based on images by using computer vision manners has been proposed in this paper. Since the weather is affected by many factors, features that can accurately represent various weather characteristics are difficult to be extracted. Therefore, in this paper, convolutional neural networks (CNNs) are applied to settle this problem. Features of extreme weather and recognition models are generated from big data. Moreover, a large-scale extreme weather dataset, "WeatherDataset", has been collected, in which 16635 extreme weather images are divided into four classes (sunny, rainstorm, blizzard, and fog), and complex scenes are coverd. A recognition model for extreme weather is obtained through two steps: Pre-training and Fine Tuning. In Pre-training step, ILSVRC-2012 Dataset is trained to obtain the model of ILSVRC using GoogLeNet. A more accurate model for extreme weather recognition is obatined by further fine-tuning GoogLeNet on WeatherDataset. The experimental results show that the proposed method is able to achieve a high performance with the recognition accuracy rate of 94.5% and can meet the requirements of some real applications.