A Method for Measuring Visibility under Foggy Weather for Expressways Based on Siamese Network

Accurately identifying highway visibility levels in foggy weather from surveillance video is important for intelligent highway supervision. Aiming at the problems of low accuracy, slow rate, and weak generalization of the current visibility recognition methods on highways, a visibility recognition method based on Siamese network is proposed, focusing on the optimization of the image feature extraction module and the fog visibility level recognition. The image feature extraction module adopts the improved VGG16 network as the backbone network. In order to enhance the ability of the network to extract important features from the global information of the image, a convolution block attention module is added to the five blocks of the VGG16 network to emphasize the effective features and suppress the useless features. To improve the generalization ability and training rate of the network, a filter response normalization layer is added after the convolutional layer of the network to remove the differences between the dimensional data. In order to solve the redundancy problem of network weight parameters and prevent overfitting, global average pooling is used to compress the output feature map directly into a 1×1 vector instead of the first two fully connected layers in the VGG16 network. A Siamese network is adopted as the main framework of the fog visibility level recognition module, and the effective features extracted by the image feature extraction module are propagated forward. The distance measurement method is utilized in the contrastive loss function to assess the similarity between input image pairs in a high-dimensional space for fog visibility level recognition. Experiments are conducted based on a dataset of actual foggy images collected from August 2022 to January 2023 on highways in Shaanxi Province. The experimental results show that the recognition accuracy of the proposed method is 90.3%, which is an improvement of 20.4%, 18.9%, and 18.0% compared to the single networks AlexNet, ResNet50, and VGG16, respectively. It is also an improvement of 16.2%, 11.0%, and 5.4% compared to the Siamese networks Simaese-AlexNet, Simaese-ResNet50, and Simaese-VGG16, respectively, which constructed based on single networks as benchmark models. In conclusion, this method exhibits a high accuracy, which contributes to enhancing the intelligent supervision capabilities for foggy weather conditions on highways.