Lane Recognition Algorithm Using the Hough Transform Based on Complicated Conditions

At present, most lane line detection methods are aimed at simple road surface. There is still no good solution for the situation that the lane line contains arrow, text and other signs. The edge left by markers such as arrow and text will interfere with the detection of lane lines. In view of the situation of arrow mark and text mark interference between lane lines, the paper proposes a new processing algorithm. The algorithm consists of four parts, Gaussian blur, image graying processing, DLD-threshold (Dark-Light-Dark-threshold) algorithm, correlation filter edge extraction and Hough transform. Among them, the DLD-threshold algorithm and related filters are mainly used to remove the identification interference between lane lines. The test results on the Caltech Lanes dataset are given at the end of the article. The result of verification of this algorithm showed a max recognition rate of 97.2%.


Introduction
In recent years, traffic accidents caused by improper driving of drivers or fatigue driving have made the field of safe driving [1] and automatic driving [2] a hot field of research.  [7] with Hough transform to detect and mark lane lines.
In the case of no other sign interference in the middle of the lane line, the conventional preprocessing method combined with the Hough transform can detect and identify the lane line very well. However, if there is an arrow mark or a text mark in the middle of the lane line, the common preprocessing method will leave some noise information to interfere with the Hough transform [8] [9]. The Hough transform will produce unstable results under complicated conditions. The detection result of the lane line will be inaccurate.
In this paper, we propose a simple algorithm to eliminate inter-line marker interference. The method is divided into four parts. First, it uses Gaussian blur to remove the noise points, and then convert it to grayscale. After, DLD-threshold algorithm and correlation filter are used to remove the interference area in the middle of the lane line and extract the edge of the lane line. After, by the use of Hough transform, a linear cluster is obtained to determine the lane line distribution. The test results on the Caltech-lane dataset are provided at the end of the article and can be found to perform well in complicated conditions.

Gaussian Blur
Gaussian blur [10], also known as Gaussian smoothing, often used in the preprocessing stage of image processing and computer vision algorithms. In this paper, it is used to preprocess road images, smooth out sharp noise points, and prevent noise points from affecting the judgment of the Hough transform line in the following text.     Figure 2 shows the output map after Gaussian convolution filtering.

Color Image Gray Processing
When the camera captures a color image, it contains three components of R, G, and B, which takes up a large space and is easily interfered by external brightness. The grayscale image has only one component and takes up little space, at the same time, considering that the human eye responds differently to different color bands. We assign different weight values to the original three channels to obtain the gray image in Figure 3.

DLD-Threshold Algorithm
In order to eliminate the interference of road signs and other signs on lane line detection, we use the DLD-threshold algorithm here.
Here, we define m as the number of rows of the image, n is the number of columns of the image, span is the average Manhattan distance between the pixels at the two edges of the lane line, threshold is the difference between the lane line and the local background gray value near it, ( ) , I i j is the gray value of input ( ) , i j d p q represents the Manhattan distance between pixel i p and pixel j q , S represents the number of elements in ij span .
( ) As shown in Figure 5 and Figure 6, DLD-threshold algorithm process pseudo code and flow chart are as follow. Figure 7 show the result of the grayscale image passing through the DLD-Threshold algorithm.

Edge Extraction
In order to reduce the computational complexity, the conventional Canny edge extraction operator [11] [12] and the Sobel edge extraction operator [13] [14] are not used here, this paper use the correlation filter for unilateral extraction.
The correlation filtering algorithm [15] can be expressed as follows where f is the input image, h is the filter template and g is the corresponding output image.
Here, we use the 3 * 3 correlation filter to process image.
We use a correlation filter to extract the image at a single edge, where the amplitudes are taken as −3 and 3 respectively to enhance the edge strength. The result is shown in Figure 8.

Hough Transform
In 1962, Paul Hough proposed the concept of Hough transform [16] [17]. The Hough transform is a parameter estimation method that uses voting to obtain a desired detection object, and is suitable for lane detection. The essence is to map the coordinate space in the image into the Hough parameter space [18], and analyze the Hough space data by point-line duality to detect the geometry. The Hough transform can be used to detect curves, circles, lines, etc. The most widely used of Hough transform is to detect straight lines in a picture.  In the Cartesian coordinate system, the linear equation is generally expressed as the following form y kx b = + (13) In this equation, k is the slope and b is the intercept. Tacking into consideration this aspect, other parameters are proposed, denoted as ρ and θ , which are polar coordinates.
[ ) As shown in Figure 10, the ρ parameter represents the distance between the line and the origin, and the θ parameter represents the angle of the vector We do the same operation described above for points of interest in the ROI area from the image. If there are multiple curves corresponding to different points intersecting in the coordinate system ρθ , it means that there is a straight line that can connect the multiple points. We can get the main lane line in the image by selecting the line with the intersection number in the Hough space. Also, the number of intersections must be greater than zero and greater than a predetermined threshold.
In addition, we may encounter some frame pictures without any mark but noise interference. We can also use the information of the previous frame in the

Experiment Result
We use the Caltech Lanes dataset [19], the image of 640 × 480 and approx, 1120 frames to validate the proposed algorithm. The algorithm was implemented by using MATLAB R2015b Intel (R) Core (TM) i5-6500 CPU@3.20 GHz, 8GB RAM, the Windows 7 Ultimate PC.
The following pictures in Figure

Conclusion
This paper presents a lane line detection algorithm under the complicated conditions. The algorithm uses Gaussian blur, image graying, DLD-threshold algorithm, correlation filter and Hough transform to determine the distribution of lane lines. The experimental results show that the method can work effectively and accurately, and obtain good results, both in the case of complicated road information and simple road information. Of course, this paper doesn't consider the impact of strong light and unclear road markings on the road environment.
In the future, we can combine various conditions to continue to improve lane detection.

Conflicts of Interest
The authors declare no conflicts of interest regarding the publication of this paper.