The Design And Implementation Of Lane Detection And Warning System Based On Machine Vision

Due to frequent traffic accidents in recent years, people pay more and more attention to the safety of traffic. Lane detection and warning system has become an important part of the IVSS (Intelligent Vehicle Safety System). IVSS, usually on the vehicle, loads image processing system or detects system to improve the driving security, such as a sensor which can achieve the use of offset warning function, or a camera shooting road image to realize the function of lane detection to reduce traffic accidents. Lane detection occupies an important aspect of IVSS.On the basis of the previous study of lane detection and warning system, paper combines itself with the actual condition of the system. First of all, system obtains images on the lane through the camera to do image preprocessing. And then it selects the improved Sobel operator to extract the lane edge, establishes lane line model, adopts the improved Hough algorithm to fit lane, and establishes a simple and effective lane departure warning model. Finally the system applies the "SIFT+SSDA" algorithm to match and fuse the binocular visual image, and it runs in the embedded platform.In image preprocessing module, the image format is YUV, which needs to be converted to RGB format in order to get a better effect of gray level, and the small amount of information about grayscale image is easy to handle. Image binarization is the pretreatment operation for image enhancement processing and edge extraction. Filtered by the gradient operator, the image noise has been suppressed. Due to the low efficiency of progressive scan on lane image, the region of interest(ROI) is set according to the range of lane. The improved Sobel operator is adopted in the ROI to extract edge of lane and the image enhancement processing is to improve the edge of the lane line, which fully retains the lane information.In lane detection and departure warning module, the lane line model is established based on the relevant characteristics of the ideal straight line and curved lanes. When the lane line is fitted, the improved Hough algorithm is used to deal with it. For straight road line fitting, lane image is divided into two constraint regions, and the constraint condition is established, which effectively reduces the interference of the non-edge line by judging whether meet the constraint conditions. For curved lane line fitting, the traditional Hough algorithm is combined with a hyperbolic model. The key parameter K is changed according to the characteristics of curvature changes, which can effectively reduce the number of fitting and searching curvilinear. The distance and range of the included angle is set between the current vehicle and the reference line to establish a simple and effective model of lane departure warning.In lane matching and fusion module, after the lane image is fit, SIFT algorithm is used in scale space to detect the extremum point location and determine the direction of feature points. And the Euclidean distance is used to match feature points for the first time. The results show that there will be false matching and too complicated feature points, thus an improved adaptive threshold SSDA algorithm combined with SIFT is used for fine matching. The algorithm will divide the template into a plurality of small modules, with the utilization of processor, multi-task and multi-thread characteristics, and is parallel to match a number of smaller modules. Finally, images is fused to display in the LCD.Experiments show that the algorithm can realize the lane detection and alarm when the deviation occurs. The entire system is based on the simulation on the MATLAB, and runs in the embedded system platform. In general, the running efficiency meets the real-time requirement, and achieves the expectation. Compared with the traditional lane detection algorithm, the algorithm in this paper has better robustness and real-time performance, and it ensures people’s driving safety.