Study on Road Weather Recognition Method Based on Road Segmentation

In order to realize the accurate recognition of weather images in road scenes, a road weather recognition method based on road segmentation is proposed, and a sort of road segmentation fusion network (RSFN), with the overall road weather image features and road features, is established by designing a method for extracting road area characteristics combining with the semantic segmentation model. First, the original images are preprocessed through the road segmentation network to obtain the binary images, and the road area information is obtained by using the convolutional feature mask (CFM). Subsequently, a convolutional neural network, which is composed of overall network branches and road network branches, is established and used to extract the overall image area features and focus on extracting the road weather features respectively. In view of the extracted irregular road characteristics, the overall image features and the road local features are fused with CFM. Finally, the weather recognition of key road areas is carried out through a fully connected layer, and the recognition of 5 types of weather (cloudy, sunny, foggy, rainy, and snowy) is realized considering the overall weather recognition. A road multi-class weather dataset (RMWD) is established by collecting real surveillance videos of expressway in multiple urban areas with different road sections and weather conditions, and compared with different network models on testing results. The result shows that (1) under the situation of decrease in parameters and computations, the RSFN weather recognition algorithm has accuracy rate and recall rate of 85.40% and 80.30% that improved by at least 3.97% and 3.86% respectively; (2) the key areas of extracting features from network models are placed in roads by using the road weather recognition method based on road segmentation, and the effective extraction of road weather features is realized by using RSFN algorithm, which can be applied to real-time and accurate weather recognition in road scenes effectively.