Research On Detecting Road Signs And Obstacles Based On Image Processing

With the increase in the number of cars,driving safety has received extensive attention.The detection of pavement signs and obstacles can prompt the driver and reduce the occurrence of traffic accidents.In the continuous development of image processing technology,image processing speed and accuracy are constantly improving.This paper uses image processing technology to detect road lane lines,road signs and obstacles.The information on the road surface is detected to provide safety for the driver.There are two cases of straight line and curve on the roadway line,in this paper,the algorithm of Hough transform and Nessel model is used to detect the straight line part and the curve part of the lane line respectively.The method is not real-time,so the Shi-Tomasi corner detection method is used to match the feature points in the image,which can improve the real-time and robustness of the detection.Because there are not many types of pavement markings,this paper uses Hu invariant moment and Markov ranging to detect and identify pavement markings.Firstly,the contour of the road marking is extracted,and then the seven Hu invariant moment values are obtained.The seven Hu invariant moment values are compared with the sample library for Markov distance,and the category of the object to be tested is identified according to the similarity.It is very difficult to detect pavement obstacles by monocular vision,so this paper uses binocular stereo matching algorithm to detect pavement obstacles.The census cost calculation is performed on the two images after denoising,after calculating the cost,the semi-global matching method is used to match,and the image depth information is acquired,thereby detecting the road surface obstacle.