Lane detection algorithm based investing

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The lane detection technique is used to control the self-driving car to keep its lane in a designated direction, providing a driver with a more convenient and safe assistant function [ 2 , 3 ]. In general, the road lanes can be divided into two types of trajectories, that is, a curved lane and a straight line [ 4 ].

In the literature, several methods were introduced for the lane detection process as shown in Figure 1. However, most of those methods usually detect only a straight lane by using an original image obtained from a front view image. With the straight lane detection, we can only recognize a near view road range, but it makes it difficult to cognize a road turning in a curved lane.

In addition, when we use front view camera images as original image source used in the detection process, the detection of curved lanes is not trivial but becomes very difficult leading to a poor detection performance. Figure 1 Top view image from a front view camera. In this paper, an effective lane detection algorithm is proposed with an improved curved lane detection performance based on a top view image transform approach [ 5 — 7 ] and a least-square estimation technique [ 8 ]. In the newly proposed method, the top view image transformation technique converts the original road image into a different image space and makes it effective and precise for the curved lane detection process.

First, a top view image converted from a front view image is generated by using a top view image transform technique. After the top view image transformation, the shape of a lane becomes almost the same as the real road lane with a minimum distortion. Then, the transformed image is divided into two regions such as a near and a far section.

In general, the road shape in the near section can be modeled with a straight lane, while the shape of the road in the far section uses either a straight line model or a curved lane model [ 4 , 9 ]. Therefore, in the near section, a straight line could be transformed with a Hough transform method [ 10 , 11 ], and a parabolic model is used to find the correct shape of the lane.

On the other hand, in the far section, a curved lane model is used with a high-order polynomial and the parameters of the curved lane are estimated by using a least-square method. Finally, each near and far section model are combined together, which leads to the construction of a realistic road profile used in the ADAS systems.

Figure 2 shows the flow process of the proposed top view based lane detection algorithms in details. Figure 2 The flow diagram of the lane detection algorithms using the top view transformation and least-square based lane model estimation. The remainder of the paper is described as follows. In Section 2 , the principle of the top view transformation is explained in detail. Nor- mally, the input image is provided in RGB color format.

It means each pixel includes three primary color channels: red, green and blue. Various colors of the pixel can be produced by changing the value of those channels. Now, that makes the problem of finding the evidences of lane markings in RGB color space become extremely challenging. For example, to detect a specific color in RGB color space which is composed of three color channels, we must compare all three channels by turns. Therefore, in the case of white or yellow color of lane markings, the wide range in RGB color space of these two colors has to be covered completely.

Contrary to RGB image, grayscale image also called black-white image contains only different shades of gray. It requires less information to be provided by specifying only one single intensity value for each pixel. Besides, a feature of lane markings is that it is always in a contrast color to road surface, for example, the most popular colors of lane markings are white and bright yellow in contrast to the dark gray color of the road.

Therefore, the gray image can perfectly enhance this contrast and greatly help in improving the detection. This step can help in intensifying the evidence of lane markings and eliminating unwanted noises from road surface.

An example of smoothing or blurring image using OpenCV is in Figure 2. The result of the grayscaling step is illustrated in Figure 4.

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