Research on Lane Detection Method with Shadow Interference

Lane detection is one of the key technologies in the field of intelligent transportation. It is widely used in assisted driving systems, lane departure warning systems and vehicle anti-colhsion systems, which is of great significance for improving traffic safety. Due to the complexity and variety of road scenes, this paper studies the lane detection with shadow interference. We present an efficient and robust algorithm for detecting lanes based on the vertical direction IPM sub-picture reconstruction and its hierarchical image fusion. Firstly, the road IPM top view is obtained, and then decomposed and reconstructed by wavelet. In addition, the Canny edge detection algorithm is employed to extract edge information of the reconstructed maps. Finally, we use the improved Hough transform to detect lane lines. We present an efficient and robust algorithm for detecting lanes with shadow interference. This method greatly reduces the interference of shadows on images through wavelet decomposition and reconstruction, which can detect the lane line more accurately. The experimental results show that the algorithm can accurately detect the lane line with shadow interference and has certain robustness.