Computer Aided Drafting, Design and Manufacturing Volume 26, Number 2, June 2016, Page 18

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1 Computer Aided Drafting, Design and Manufacturing Volume 26, Number 2, June 2016, Page 18 CADDM The recognition algorithm for lane line of urban road based on feature analysis Xiao Xiao, Che Xiangjiu College of Computer Science and Technology, Jilin University, Jilin , China Abstract: The building information model/modeling (BIM) technology is currently applied in a broad range of applications and research for facility management (FM), while the BIM-based mobile FM is difficult owing to various factors and environments. For example, the mobile applications usually require frequent cross-equipment compatibility. This paper proposes a reasonable BIM-based FM cross-platform framework and develops a mobile application on the basis of an existing BIM-based FM system. The developed mobile application is applied in a case study of a metro station project in Guangzhou to verify its effectiveness in FM practice. It helps maintenance staff in viewing BIMs, accessing related information, and updating maintenance records in a unique platform. The test results demonstrate that the proposed BIM-based cross-platform framework meet the FM application requirements and supports the extension of FM functions. Key words: facility management; building information model/modeling; mobile application; cross-platform; hybrid application 1 Introduction Urban transportation and people s life is closely linked. The car completely covered the contemporary people's life, makes people's life more convenient and fast. But it also causes many problems, such as traffic jams and traffic accidents, etc. In particular, the damage of traffic accidents to the family and society is especially serious. Recently, most of traffic accidents are caused by vehicles driving off the track. If we can detect and identify the lane line and remind drivers when the vehicles get out of their way, it will significantly reduce traffic accidents. At present many countries are doing a lot of research work on the lane line detection, and they have found a lot of lane line detection algorithms. Those algorithms use different path models and recognition technology. They can be roughly divided into two categories, one is the method based on the feature, the other is the method based on model. The method based on the feature mainly uses the image features of roads, such as edge, color and texture, etc. For example, in the reference [1], using color features with the method of the color space of color clustering recognition lane line, and then fitting the lane line. But the efficiency of the algorithm is low. In the reference [2], it gives a method based on edge detection, then uses the method based on a straight line to detect lane line. The method based on model is using a priori knowledge of the road to describe the lane line with a specific geometry model. The common road models include straight line model, parabolic model, hyperbolic model and spline curve model, etc. For examples, in the reference [3], the main work is estimating the parameters of hyperbola in order to use the hyperbolic model to describe the lane line. In the reference [4-5], it uses consistency with random sampling algorithm fitting the B-spline curve to get parameters of the lane line model. In the lane line detection, most of the captured images have interference background. Especially for the city road image, a lot of interference is created by the houses, trees, street lamps, etc. If we can remove the interference background, the detection accuracy can be greatly increased. In most instances, the physical properties of the lane line are not considered when the lane line is detected. Different types of lane line have different meanings, it is very meaningful to identify its physical properties in the intelligent transportation system. In this article, we detect lane line by analyzing the characteristics of the lane line. First we carry out the ROI(Region of Interest) initialization, then apply a more suitable method for image graying. Further we Project item: Supported by National Natural Science Foundation of China (NSFC) ( ). Corresponding author: Che Xiangjiu, Male, Ph.D., Professor. chexj@jlu.edu.cn.

2 Xiao Xiao et al., The recognition algorithm for lane line of urban road based on feature analysis 19 use the method based on the lane line features analysis to extract the lane line area. Finally we use Hough Transform algorithm with constraints to detect the lane line, and identifying the lane line physical properties. 2 Image preprocessing Before detecting the lane line in the image, we should do the image preprocessing. The main tasks are the ROI initialization, image graying and extracting feature area. 2.1 ROI initialization In order to reduce the processing time while keeping good performance, the ROI Initialization of the input image is necessary. Generally speaking, the lane line often locates in the lower part of the picture. That is the reason why our ROI is selected on the lower half of the image. In our testing process, there are two lane lines: left and right. So we divided the ROI into the left and right sub-regions. In this paper, the size of each picture is The original image and the ROI as shown in Fig.1 and Fig.2. Fig. 1 Original image. Fig. 2 ROI. 2.2 Image graying Most of the color images are RGB color space, we need to convert them into grayscale image. On the road, the colors of the lane marks are usually white or yellow. In order to enhance the character of the lane-mark information, we use a more efficient method [6] as: Gray = R G B (1) The range of the R, G and B is within [0, 255], respectively represents the value of the Red, Green, and Blue components. Gray is the value after graying. The result after processing is shown in Fig.3. Fig. 3 Grayscale. 2.3 Extracting feature area Before the lane line detection, we need to extract the feature area of the lane line. In this article, we adopt the edge detection algorithm to extract the edge character of ROI. Then we put forward a method to remove those pixels that do not belong to the edge of the lane line by the analysis of the feature of lane line, namely the interference background. Thus we can get more accurate lane line feature area. In this article, we use Canny edge detection [7]. The Canny edge detection is a multi-level edge detection method which was developed by Canny J in 1986, aiming to find an optimal edge detection algorithm. Canny edge detection algorithm can obtain better

3 20 Computer Aided Drafting, Design and Manufacturing (CADDM), Vol.26, No.2, Jun results even in the case of low contrast. Steps of Canny edge detection: (1) Elimination of noise edges: using Gaussian filter on the image. Image Gaussian filter can use the two one-dimension Gaussian kernels to do convolution twice, also can use a two-dimensional Gaussian kernel to do convolution once. One-dimensional Gaussian function and two-dimensional Gaussian function are shown as: 2 2 2σ x 1 K = e 2π σ σ x y 1 K = e 2π 2 σ (2) (3) (2) Computing the gradient and direction: we use the convolution operator to do convolution in the image on the x direction and y direction. The convolution operator is represented as G x and G y. We use the Eq.(4) and (5) to calculate the gradient and direction of each pixel in the image: 2 2 x y G G G (4) GY θ =arctan (5) GX The gradient direction generally is chosen from these four possible angles: 0, 45, 90 and 135. (3) The non-maximum inhibition: excluding the non-edge pixels, just keeping some candidate. (4) Hysteresis thresholds: we select two thresholds. If a pixel value in the picture is greater than the high threshold, the pixel is on the edge. If a pixel value in the picture is less than the low threshold, the pixel will be excluded. If the value of a pixel in the picture between the two thresholds, the pixel will be preserved when it connect to a pixel which is greater than the high threshold. Canny edge detection processing results are shown in Fig.4. Fig. 4 Canny edge detection. There are lots of edges that do not belong to lane lines in Fig.4. The original method [8] did the detection on the results as Fig.4 directly. It not only influenced the correct detection of the lane line, but also made heavy workload. Through the above description and the analysis of the feature of lane line, in this paper, we put forward a method based on contour map to remove interference background. The contour map is put these pixels which are detected by edge detection together to be a whole. If the interference background can be removed, the accuracy and efficiency of detection can be improved [9]. The specific method is as follows: (1) Analyzing the area of contour map of lane line and interference background. Lane line is a relatively independent and obvious individual, its contour area is bigger than the area of the interference of background. So we put forward a method to choose contour maps by the comparison of contours and a threshold. The calculation of contour area is the number of pixels contained in contour map. A contour map will be retained if its area is greater than the threshold, while a contour map will be removed if its area is less than the threshold. (2) Analyzing the width-length ratio of lane line and interference background. In normal circumstances, the lane line is parallel with the movement trace of the car, but the lane line is tilted in driving view. Even so, its width-length ratio of edge contour area should be within a certain range. The width-length ratio refers to the peripheral rectangle of the contour map. We will reserve those pixels if the width-length ratio is in the range, otherwise, we will remove those pixels. According to the analysis of lots of pictures, we found that the contour area of some interference background is very small comparing with the lane line. And the width-length ratio of lane lines should be limited. We should remove those contour maps if the width-length ratio is too large [10]. After the experiments of 300 pictures(the size of those pictures is ) we collected, we found that the results are optimal when the threshold of contour area is set to 40. Then we did subsequent experiments to those pictures which were processed above, when the threshold of width-length ratio is set to 3 we can get the best results. The experimental results as shown in Fig.5.

4 Xiao Xiao et al., The recognition algorithm for lane line of urban road based on feature analysis 21 Fig. 5 After elimination of interference background process. By contrast Fig.5 with the Canny transformation result (as shown in Fig.4), obviously we can get that a lot of interference background on the picture has been removed. The preserved edge character fits the requirements more. The error brought by the interference background will be reduced, and the efficiency and accuracy of lane line detection can be improved. 3 Lane line detection and recognition 3.1 Hough transform The lane line detection based on Hough Transform is a commonly used method. Hough Transform is a kind of space transform, it implements the mapping from image space to the parameter space. A straight line under the x-y coordinate system can be represented by y = kx + b. The main idea of the Hough Transform is to exchange the equation parameters and variables, with x and y as known quantity, k and b as variables. The parameter space coordinates are expressed as ρ and θ of polar coordinates. The formula equation is shown as follow: ρ = xcosθ ysinθ (6) According to the Eq.(6), points of straight line of a image are mapped into sine curve of ρ-θ space, and these sine curves at one point, as shown in Fig.6. In Fig.6, a few points on a straight line of x-y coordinate system are shown in Fig.6(a), and the corresponding sine curves of the ρ-θ space are shown in (b). Several properties of Hough Transform are as follows: (a) (b) Fig. 6 Example of Hough Transform. (a) x-y coordinate system; (b) ρ-θ space. (1) A point of the x-y space corresponds to a sine curve of the ρ-θ space. (2) A point of the ρ-θ space corresponds to a straight line of the x-y space. (3) N points on a straight line of x-y space correspond to n sine curves at a common point of ρ-θ space. Secondly, based on the removal of the interference of background, we choose the longest straight line as the lane line in two ROI sub-regions respectively. After these operations the result of lane line detection is shown in Fig.7. In this paper, the specific process by using Hough Transform with constraints is as follow: According to the feature of the lane line, firstly, in the picture, the lane line hardly appears in horizontal direction, so we should delete the straight line whose slope between the [ 0.1, 0.1] [10]. Fig. 7 Result after Hough Transform.

5 22 Computer Aided Drafting, Design and Manufacturing (CADDM), Vol.26, No.2, Jun The identification of physical properties of the lane line As the different types of lane lines have different meanings, the identification of physical properties of the lane line has a great significance to intelligent transportation system. We mainly identify the color of lane line and distinguish the lane line is dotted line or full line. First, the beginning and end of the lane line can be gotten through lane line detection. It is set to p 1 (x 1, y 1 ) and p 2 (x 2, y 2 ) respectively, and then extracting the neighborhood of lane line [11]. The coordinates of four vertices of neighborhood are (x 1, y 1 OFFSET), (x 1, y 1 +OFFSET), (x 2, y 2 OFFSET) and (x 2, y 2 +OFFSET). According to the size of the image, the OFFSET is set to 10, it means the width of the neighborhood is 20 pixels.when we are identifying the color, because of RGB color space is not a uniform color space, it is not suitable for color identification. In this paper, we use the HSV color space [12]. HSV color can be divided into three components: hue(h), saturation(s) and luminance(v). The transformation from RGB color space to HSV color space is shown as: V max R, G, B / 255 (7) V min{ R, G, B}, if V 0 S V (8) 0, otherwise 60 G B, if V = R V min min R, G, B 60B R H 120, if V = G V min min R, G, B 60R G 240, if V = B V min min R, G, B (9) If H<0, then H=H+360. In OPENCV, H=H/2, S=S 255, V=V 255. The calculation of HSV component within the neighborhood is used for color identification. Then according to the number of the lines of identified color in proportion to the number of rows of the whole neighborhood, we can judge whether the lane line is dotted line or lines. The recognition algorithm is shown: begin for each line of the field do if(95<=componenth<=110&& componentv>40&& components>40) yellownum=yellownum+1. GetOneYellowPixelThisRow=true. end if if(componentv>=210) whitenum=whitenum+1. GetOneYellowPixelThisRow=true. end if end for if(yellownum>=whitenum) if(yellownum/sumlinesnum>0.7) LineColor=yellow and LineType=FullLine. else LineColor=yellow and LineType=DashLine. end if if(yellownum<whitenum) if(white/ sumlinesnum >0.7) LineColor=white and LineType=FullLine. else LineColor=yellow and LineType=DashLine. end if end The threshold value in the table H, S and V is the average of more than one lane image. 4 The experiment results and analysis In this paper, the experimental environment we use is VS2010 and OPENCV2.4.4, the machine configuration is i3 processor, RAM 4 GB. The image from the collection images focused on the road of Changchun city of Jilin province. We selected a large number of images with various road background, the experiment results and analysis is shown as follows. 4.1 The lane line detection results As shown in Fig.8, we choose four groups of images with four kinds of methods to test, method 1 is the original method [8] which does not do the removal of interference background, results such as Fig.(a1), (b1), (c1) and (d1). Method 2 [13] is the feature extraction algorithm based on local information, results such as Fig.(a2), (b2), (c2) and (d2). Method 3 is based on the raster scanning manner, after the straight line detection, to scan every single line. The road lane lines are detected when they meet the lane line feature, results such as Fig.(a3), (b3), (c3) and (d3). Method 4 is an improved algorithm proposed by this paper which removes interference background, the results of experiment are Fig.(a4), (b4), (c4) and (d4). From the experiment results above, we can obviously find that the former three methods can not get satisfied results, while the method proposed in this paper can accurately detect various types of lane line, the accuracy is obviously improved.

6 Xiao Xiao et al., The recognition algorithm for lane line of urban road based on feature analysis (a1) (a2) (a3) (a4) (b1) (b2) (b3) (b4) (c1) (c2) (c3) (c4) (d1) (d2) (d3) (d4) Fig. 8 Result of lane line detection. 23

7 24 Computer Aided Drafting, Design and Manufacturing (CADDM), Vol.26, No.2, Jun In order to analyze the computation time of the four different methods, we use the same group of images to test the four methods, and the result is shown in Table 1. Table 1 Computation time Method Time(ms) Average time(ms) Method 1 [8] Method 2 [13] Method 3 This paper The range of computing time and average time shown in Table 1 is counted from reading of images to getting the results of lane line detection. We can clearly see that the proposed method in this paper uses the shortest computing time and average time, and gets a great improvement compared with the method of 2 and 3. According to the experiment results above, the improved lane line detection method proposed by this paper not only has obvious increase in accuracy, but also has the very big enhancement on the calculation time. 4.2 The lane line identification of physical properties Through the algorithm described in table 1, this paper can accurately identify the color of the lane line and whether the lane line is dotted line or full one. The experiment results on the identification of lane line color and type are shown as Fig.9-12: Left yellow solid lane line Right white dotted lane line Fig. 9 Yellow solid lines and white dotted line. Left white dotted lane lines Right white dotted lane line Fig. 10 White dotted line and white dotted line. From the pictures above, we identify the different types of lane lines, such as white solid lines, yellow solid lines, white dotted lines, yellow dotted lines. From the experiment results, the proposed algorithm in this paper not only can accurately detect its specific location, but also can identify the type of lane line accurately.

8 Xiao Xiao et al., The recognition algorithm for lane line of urban road based on feature analysis 25 Left white solid lane line Right white solid lane line Fig. 11 White solid line and white solid line. Left yellow dotted lane line Right white dotted lane line Fig. 12 Yellow dotted line and white dotted line. 5 Conclusion Through the experiment results in section 4,the algorithm in this paper can improve the accuracy and has a high efficiency by improving the extraction of feature area of lane line. Simultaneously by identifying the physical feature of the lane line, we can identify the type of the lane line effectively, which means a lot to the follow-up work, such as pre-warning in the driving process. We hope we can get a more efficient algorithm in the subsequent work, making the intelligent transportation system more perfect. References [1] Ma C, Mao L, Xie M. Lane detection using heuristic search methods based on color clustering [C]// International Conference on Communications, Circuits and Systems Proceeding (ICCCAS). New York: IEEE Press, 2010: [2] Chang C Y, Lin C H. An efficient method for lane-mark extraction in complex conditions [C]//IEEE International Conference on Ubiquitous Intelligence and Computing. Los Alamitos: IEEE Computer Society, 2012: [3] Aly M. Real time detection of lane markers in urban streets[c]//ieee Intelligent Vehicles Symposium Eindhoven University of Technology Eindhoven. New York: IEEE Press, 2008: [4] Wang Y, Li B, Fairhurs M. Robust road modeling and tracking using condensation [J]. Intelligent Transportation Systems IEEE Transations on, 2008, 9(4): [5] Wang Y, Teoh E K. Dynamic b-snake model for complex objects segmentation [J]. Image and Vision Computing. 2005, 23(12): [6] Sun T Y, Huang W C. Embedded vehicle lane-marking tracking system [C]//IEEE International Symposium on Consumer Electronics. New York: IEEE Press, 2009: [7] Canny J. A computational approach to edge detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6):

9 26 Computer Aided Drafting, Design and Manufacturing (CADDM), Vol.26, No.2, Jun [8] Liu N, Cao J M, Wang X L. Vision-based lane detection algorithm [J]. Electronic Test, 2013(4): [9] Li M, Shi D, Cheng D, et al. Vision-based robot navigation using parallel Hough transform [C]// International Conference on Machine Learning and Cybernetics (ICMLC). New York: IEEE Press, 2013: [10] Wang J, Zhang Y, Shi X Y, et al. A quick scan and lane recognition algorithm based on positional distribution and edges features [C]//2010 International Conference on Image Processing and Pattern Recognition in Industrial Engineering. Xi an, China: International Society for Optics and Photonics (SPIE), 2010: 78202F-78202F-8. [11] Gu X L, Yang M, Wang B, et al. Lane detection and recognition based on around-view system [J]. Huazhong University of Science and Technology(Natural Science Edition), 2013, 41(Sup.l): [12] Zhang G Q, Li Z M, Li X W, et al. The color image segmentation research in the HSV space [J]. Computer Engineering and Applications, 2010, 46(26): [13] Liu G R, Li S T, Liu W R. Lane detection algorithm based on local feature extraction [C]//2013 Chinese Automation Congress(CAC). New York: IEEE Press, 2013, Xiao Xiao is studying in College of Computer science and Technology, Jilin university, Changchun, China. Her research interests include the wavelet transform, imaging processing, and especially focus on the detection of lane line. She born in 1993, master candidate. Che Xiangjiu is currently a professor in the College of Computer Science and Technology, Jilin University. He was promoted to a professor in September 2006, then in June 2012 to a doctoral supervisor. He is currently a member of CAD/CG professional committee, China Computer Federation; a member of Geometric Design and Computing professional committee, China Society for Industrial and Applied Mathematics. He is currently a council member of the China Computer Federation. His current research interests include visualization computing and analysis, surface modeling and 3D reconstruction, network media technology, etc. He received the PhD degree from Jilin University in 2003.