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.

翻译：视觉监控技术是先进交通管理系统中不可或缺的功能组件，已被广泛应用于执行目标检测、跟踪与识别等交通监管任务。然而，雾、霾、薄雾等恶劣天气条件给基于视频的交通监控带来了严峻挑战。为消除恶劣天气条件的影响，我们提出了一种面向实时能见度增强的双注意力与双频引导去雾网络（称为DADFNet）。该网络由双注意力模块（DAM）和高低频引导子网络（HLFN）组成，通过联合考虑注意力与频率映射来引导无雾场景重建。在合成图像与真实世界图像上的大量实验表明，DADFNet在能见度增强与检测精度提升方面均优于现有最先进方法。此外，DADFNet在2080 Ti GPU上处理一张1920×1080图像仅需6.3毫秒，使其在智能交通系统中具备极高的部署效率。