Weather Recognition Based on Edge Deterioration and Convolutional Neural Networks

Weather recognition is of great significance in traffic safety, environment and meteorology. However, the visual image features of the weather are highly abstractive, and the traditional method of weather recognition has a high computational complexity and low accuracy. In this paper, the edge deterioration phenomenon is introduced in convolution neural network (CNN) to solve the problem that common CNN cannot distinguish the specific weather. The proposed method used Mask R-CNN to extract the regions of interest including the foreground and foreground edges in the image, and superimposed them into the same-scale matrix and then input them into the network for classification. Outdoor traffic image experiments showed that this method can effectively improve the classification accuracy of the four weather conditions (sunny, foggy, rainy and snowy).