Lane detection is crucial for driver assistance systems. However, road scenes are severely degraded in dense fog, which leads to the loss of robustness of many lane detection methods. For this problem, an end-to-end method combining polarimetric dehazing and lane detection is proposed in this paper. From images with dense fog captured by a vehicle-mounted monochrome polarization camera, the darkest and brightest images are synthesized. Then, the airlight degree of polarization is estimated from angle of polarization, and the airlight is optimized by guided filtering to facilitate lane detection. After dehazing, the lane detection is carried out by a Canny operator and Hough transform. Having helped achieve good lane detection results in dense fog, the proposed dehazing method is also adaptive and computationally efficient. In general, this paper provides a valuable reference for driving safety in dense fog. (C) 2020 Optical Society of America
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