Research On The Detection Method Of The Lane Line Of Unmanned Vehicles

As a key factor of unmanned system and drive assistance system,the study of the lane detection method has attracted much attention.In this paper,the real-time straight line detection and curve detection are realized from the traditional detection method,and the rough location of the lane area is realized from the machine learning method.The work is divided into the following sections:(1)A fast lane detection method based on projection transformation and three kinds of lane filtering mechanism are proposed and realized.When the line is worn,the edge detection in the traditional method can not do anything.When the bright light and the shadow are occluded,the false detection rate of the binarization method in the traditional method will also increase,so this paper proposes to extract the candidate points of the lane by using the projection information in the image.Aiming at the phenomenon of "near and far small" in the image of the lane,the integral projection is used to realize the segmented projection transformation,which is equivalent to realizing the variable resolution detection of the lane.The Hough transform method based on angle estimation is used in this paper.The method of contrast judgment,vanishing point estimation and linear clustering are used to deal with the results of Hough transform.The experimental results show that the proposed method is robust to strong light,shading and abrasion,and can satisfy the real-time requirement of unmanned vehicles while ensuring accuracy.(2)A method to detect the bend of roadway based on subsection vanishing point estimation is presented and realized.According to the characteristics of multi-vanishing point of the curve,this method uses the method of segmented Vanishing Point Estimation to judge the curve.In the process of curve detection,this method uses the method of segmented Hough transform to extract the straight line,and obtains the candidate point of the curve by sampling,and then through the least square method and cubic spline interpolation Method respectively.In order to reject some unreasonable curve results after fitting,the method uses a camera calibration result to post-match the fitted curve.The experimental results show that the method can discriminate and test the curve better,at the same time,it is found that the least squares method is more robust and the error of cubic spline interpolation method is smaller.(3)A machine learning-based rough location method for candidate areas of lane candidates is proposed.Because the lane has obvious gradient and edge features,this paper adopts the HOG and Haar feature to process the image of the lane line.In this paper,the HOG gradient histogram feature of the lane line is firstly extracted and the training classification is carried out by using the support vector machine SVM.At the same time,Haar feature of the lane line is extracted,and it is realized with the cascade classifier Ababoost.Experimental results show that Haar feature with Adaboost cascade classifier has higher detection rate.(4)Finally,the hardware system and software system design of the unmanned vehicle experimental platform in this project has been introduced.The algorithm has been verified by experiments in the unmanned vehicle system,and has achieved good application effect.