| description abstract | Frequent highway accidents occur in the Guizhou region, among which poor visibility due to fog is one of the main causative factors. In this region, traditional large-scale, high-computational-power fog monitoring systems are difficult to install and have high costs due to complex terrains, high altitudes, and winding roads, causing traffic management departments to fail to obtain fog information accurately and timely, which undoubtedly becomes a significant safety hazard. To solve this problem, this study proposes a fog monitoring solution based on the lightweight deep learning model ABNet. The solution first preprocesses the input images, including generating the fog concentration distribution map using the fog imaging model, and obtaining the high-frequency component image using filters based on the 2D discrete wavelet transform. Subsequently, these two processed images and the original image are fed into the three branches of the ABNet for training to fully extract fog concentration and high frequency information, thereby improving model performance and prediction accuracy. The ABNet model parameters only require 38.52MB, and the computational complexity is a mere 1.71GFLOPs, effectively solving the limited storage and computational resources problem in edge computing. The model was evaluated using the Guizhou highway fog weather data set, and ABNet exhibited impressive performance with a composite classification accuracy as high as 92.3%, reaching 92.4% in average precision rate and 92.3% in average recall rate. In comparison, the performance of models like VisNet, VGG16, EfficientNetV2, and Swin Transformer V2 seemed inferior. The experimental results validated the excellent performance of the ABNet model in terms of accuracy and efficiency. The ABNet model in this study, with its lightweight deep learning design, small parameter scale, and lower computational power requirements, provides a solution suitable for complex terrains and practical environments of edge computing devices, and it provides vital technological support to improve traffic safety on the highways in the Guizhou region. | |
