Lane Detection Algorithm Based On Optimal-denoised And Geometric Moment Sampling

The rapid advances of artificial intelligence(AI)over the world has raised a global boom in the research of its associated applications.Smart cars and Advanced Driver Assistance Systems(ADAS)as one of its significant branch,is concerned and researched by relative experts.Compared to man-driving vehicles,ADAS can make more timely and accurate judgments and reaction to hazardous road conditions,and reduce the incidence of city traffic accidents to a certain extent.As one of the core technology for Lane Departure Warning System(LDWS),lane detection has fatal research significance.Existing lane detection algorithms come with costs regarding the required memory and the complexity of the algorithms limiting ways of generating versions adaptive to challenging road environments and portable low-power consumption equipments.We propose a new solution for lane detection based on optimal denoising geometric moment sampling.First,the current frame of a recorded video is processed by selecting potential regions of interest(i.e.potentially containing lane markings)based on piecewise sampling.Then,the visible road surface is segmented by labeling connected components of equivalent pixel values using an image binarization procedure.By analyzing the different order of geometric moments of connected components in the lane region,the centroid and the direction angle of detected parts of lane marking segments are calculated.We combine these calculated parameters;a lane marking segment is finally detected via piecewise representing to the lane marking segment.The key advantages of the proposed method lie in the following parts:1.Piecewise sampling lane line not only solved the limit of fixed region of interest for various multi-lane models,but reduced the required memory of image pre-processing.2.By using geometric moments computation,lane segments are detected without edge extraction and lane fitting.Hence,it’s a simple,efficient and memory-saving approach,while satisfying the requirement of real-time detecting.The proposed method is evaluated in simulate and real-word situations.Experimental results under various lane shape,weather and ligiting conditions(including straight,curve,nighttime,shadow or overlap)show that our method outperforms traditional lane detection algorithms and some frontier approach in both detection rate and real time consumption.The proposed method not only has real-time performance and a high accuracy in detecting lanes of varying appearance,but also offers convenient adaptation of detection both to light-reflective road surface and further types of lighting disturbances.