001 A fast and stable lane detection method based on B-spline curve

Fit the road model
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Figure 1. algorithm structure
2.1 Road edges extraction Road edge detection is the premise of the road
In the past few years, a number of model-based s_tr_u_c_tu_r_e_r_o_a_d__de_t_e_ct_io_n__a_lg_o_r_it_h_m_s__[8_, 12] have been
978-1-4244-5268-2/09/$25.00 ©2009 IEEE
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image after MRDT. Obviously a lot of noise has been removed.
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Edge Detection
Edge map segmentation
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Lane marking extraction
2. Structured road detection algorithm
Our algorithm can be divided into three parts: The first part is to extract all of the possible road edge pixels using the Canny operator, and then denoise them by multi-regional two-threshold segmentation algorithm [12]. The second part is to get the road marking. Firstly, the image here is partitioned into a small number of horizontal sections. Then we obtain the points sets on the edges of line marking in each sections using Mid_to_Side method [11].Finally the extracted points sets are used to fit straight lines by the least square method separately. The third part is to fit the entire road. We here use the B-spline curve to fit the central line of the road. Our algorithm is described as figure 1:
Keywords: Road detection, Canny edge detection, Least square method, Open Uniform B-Spline
1. Introduction
Vision-based intelligent navigation system is an important application of computer vision. The road detection algorithm is one of the key technologies of the system. There are mainly two methods to deal with the detection of structured road: the feature-based and model-based method. The feature-based[1,2] obtains the road region by clustering or region growing approach based on the differences of gradient, color or texture between road and nonroad area. The main advantages of such methods are not sensitive to the shape of the road, and it has high robustness. But it’s sensitive to the shadows, water areas and need more calculation. Model-based [3, 7] methods begin with the hypothesis of the road model, and then match the road edge with the road model. Such methods is more comprehensive, since only few parameters need to model the road edge, so it is more robustness against noises, such as shadows, water areas, etc.
huarong_xu@
Abstract
A new structure road detection algorithm based on open uniform B-spline curve model is proposed. Firstly, canny edge detection algorithm is used to extract the road edge. Then road markings are gotten from the edge map by the least square method. Lastly, the road model (B-spline model) is fitted after the central points of the road are gotten from the lane markings. In order to accurate the position of the road bend, the algorithm introduces the maximum deviation of position shift (MDPS) method to search control points. A large number of experiments show that the algorithm is fast, stable, and flexible. It is able to meet the requirements of intelligent navigation.
A fast and stable lane detection method based on B-spline curve
Huarong Xu1 2, Xiaodong Wang3, Hongwu Huang4, Keshou Wu2 , Qiu Fang4 1. Department of Physics, Xiamen University, Xiamen, Fujian Province,361003 China; 2. Department of Computer Science and Technology, Xiamen University of Technology, Xiamen, Fujian Province,361024 China. 3. College of Computer and Communication, Hunan University, Changsha, Hunan Province, 410082, China 4. Department of Mechanical Engineering, Xiamen University of Technology, Xiamen, Fujian Province,361024 China.
detection algorithm. Currently existing edge detection operators include Canny, Sobel, Laplacian, Prewitt operator, etc. Canny edge detector has the advantages as follows: not missed the real week edge, a good positioning performance and only one response per edge. Considering the requirements of our alogrithm and the performance of operator characteristics and speed, we decided to use the Canny operator.

developed. The B-Snake based detection algorithm[8] first assumes that two sides of the road boundaries are parallel, so the problem of detecting both sides of lane markings (or boundaries) has been merged as the problem of detecting the mid-line of the lane. However, the algorithm repeatedly quoted Hough algorithm [13] which increased the computation of the algorithm. A fast hyperbolic model based algorithm is used by [11], which use Mid_to_Side marking two parallel lines. The algorithm is simple and has a stronger anti-noise performance. But the use of hyperbolic fitting algorithm for the road makes the fitted curve fluctuate largely when the road encounter more noise.