Low-light enhancement network for nighttime highway visibility estimation in foggy weather

Dense fog often appears on highways at night and early in the morning, seriously affecting visibility and posing important traffic safety risks. The surveillance images taken during this period have poor imaging quality due to low light, which brings great challenges to visibility estimation from surveillance images. To cope with this problem, we propose a deep network with low-light enhancement capability to achieve automatic visibility estimation. The network model consists of two parts: the light enhancement sub-network and the visibility classification sub-network. The light enhancement sub-network adopts the generative adversarial network architecture to generate the light-enhanced image under the constraint of discriminator loss and self-feature retention loss. The visibility classification sub-network extracts the visual features of the enhanced image and combines them with bright channels to classify through a multi-head attention mechanism, achieving effective detection of visibility. At the same time, we construct a nighttime highway fog image dataset by collecting surveillance videos of several highways in China. Experimental results on this real-scene dataset show that the proposed algorithm achieves the highest detection accuracy, which can provide technical support for the intelligent management of the highway management department.