Foggy Lane Line Detection Based on an Improved Dark Channel Prior and the Canny Operator

Abstract

Lane line detection is an important part of intelligent driving. However, the road pictures taken in haze weather have low clarity, and the loss of detailed information brings great difficulties for lane line detection. The classical defogging algorithm cannot meet the accuracy requirements of lane line detection, so this paper proposes a foggy lane line detection algorithm for improving the dark channel and Canny operator. First, the foggy image is divided into the area of interest, and then use the quadruple tree multilevel search to obtain the accurate value of atmospheric light. The restored image is image-enhanced to improve the effect of fog removal; The images are then preprocessed for edge detection using the modified Canny operator, and we use a hybrid filter instead of Gaussian filter. The maximum inter-class variance (OSTU) algorithm obtains the adaptive optimal threshold and improves the experimental performance of the algorithm. Finally, the polar angle constraint uses the Hough transformation to accurately fit the lane lines. Experiments show that the improved defogging algorithm obtains higher image contrast, clearer image, and the edge detects more preserved lane line information, and improves the recognition accuracy of vehicle lane lines.