Multi visual feature fusion based fog visibility estimation for expressway surveillance using deep learning network

Visibility in foggy weather is of great value for traffic management and pollution monitoring. However, vision-based fog visibility estimation methods are usually based on a single image to approximate the visibility in foggy weather, and most existing data-driven machine learning models struggle to capture effective features and achieve high estimation accuracy due to the severe image degradation caused by reduced visibility and lack of real scene images. Therefore, this paper proposes a novel deep learning framework based on multi visual feature fusion for fog visibility estimation, named VENet, which comprises of two subtask networks (for fog level classification and fog visibility estimation) constructed in a cascade structure. A special feature extractor and an anchor-based regression method (ARM) are proposed to help improve the accuracy. Further, a standard Fog Visibility Estimation Image (FVEI) dataset containing 15,000 images of real fog scenes is established. This dataset greatly bridges the lack of suitable data in the field of vision-based visibility estimation. Extensive experiments have been conducted to demonstrate the performance of the proposed VENet, where the error of fog visibility estimation is less than 5% at 500 m and the fog level classification accuracy is at least 92.3%. In addition, the proposed VENet has been applied on Yunnan Xiangli and Mazhao Expressway surveillance with promising performance in practice.