  Visual surveillance technology is an indispensable functional component of
advanced traffic management systems. It has been applied to perform traffic
supervision tasks, such as object detection, tracking and recognition. However,
adverse weather conditions, e.g., fog, haze and mist, pose severe challenges
for video-based transportation surveillance. To eliminate the influences of
adverse weather conditions, we propose a dual attention and dual
frequency-guided dehazing network (termed DADFNet) for real-time visibility
enhancement. It consists of a dual attention module (DAM) and a high-low
frequency-guided sub-net (HLFN) to jointly consider the attention and frequency
mapping to guide haze-free scene reconstruction. Extensive experiments on both
synthetic and real-world images demonstrate the superiority of DADFNet over
state-of-the-art methods in terms of visibility enhancement and improvement in
detection accuracy. Furthermore, DADFNet only takes $6.3$ ms to process a 1,920
* 1,080 image on the 2080 Ti GPU, making it highly efficient for deployment in
intelligent transportation systems.
