Zhang Li, Yin Zhongjun, Zhao Kaichun, Tian Han
Appl Opt. 2020 Jul 1;59(19):5702-5707. doi: 10.1364/AO.391840.
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.
车道检测对于驾驶辅助系统至关重要。然而，在浓雾中道路场景会严重退化，这导致许多车道检测方法的鲁棒性丧失。针对这个问题，本文提出了一种结合偏振去雾和车道检测的端到端方法。从车载单色偏振相机捕获的浓雾图像中，合成最暗和最亮的图像。然后，根据偏振角估计大气光的偏振度，并通过引导滤波对大气光进行优化以利于车道检测。去雾后，通过Canny算子和霍夫变换进行车道检测。所提出的去雾方法在浓雾中有助于实现良好的车道检测结果，并且具有自适应性和计算效率。总体而言，本文为浓雾中的驾驶安全提供了有价值的参考。
