Line Detection Method

Either colour or greyscale images are both viable possibilities for use in lane detection. Using colour has the advantage over greyscale in that more information is captured in the image and potentially a more reliable result can be obtained. The downside to the use of colour images is the time taken to process those images. It takes roughly three times a long to process colour images when compared to greyscale images and in applications where speed is a factor greyscale images are better suited.

Regions of interest are also widely used in the area of lane detection in videos. These are areas of the images that are searched instead of the whole image and are based on where the markings were in the previous image. This approach allows the processing to be sped up. Various shapes of ROIs have been used in a number of processes. Short horizontal lines of pixels spaced out over the projected location as specific distances corresponding to distances in the real world [9] [16] are one example. Another is a trapezoidal window based on the shape of the road that is derived from [15]. There are also small windows not unlike the scan lines but with a height component added to them.

Through research many different methods of line detection have been proposed and used in various previous applications. The most common methods being Global Threshold, Positive Negative Gradient, Top Hat, Local Threshold, Symmetrical Local Threshold and Steerable Filters. Each performs slightly differently and requires different amounts of time to complete.

The positive negative gradient method scans horizontally for dark-bright-dark gradient changes where the changes are greater than a threshold. It also checks the space between the gradient changes and this must be within a specific range to be allowed. This method works well for vertical and large radius curved lines but doesn’t function for horizontal lines.

Top hat filters perform a similar process to the positive negative gradient method. Horizontal lines of the image are convolved with a top hat shaped matrix. The resulting values are put through a threshold to find the edges [18]. This second method only works for a line of a specific width. With additional processing time the width of the line can be taken into consideration by varying the width of the convolution matrix [17].

21 Parallel features or symmetrical local thresholds ask whether the current pixel is a line by looking at the pixels a small distance to each side and then checking the intensity difference. Both single pixels [12] and averages of pixels either side [17] have been used but it depends on processing speed and also the road surface. This process is also width dependant although by making it a large distance the possibilities of ignoring a line are diminished.

There are two types of thresholding, global and local. Global creates a threshold based on the entire image and then anything over that threshold is counted as a feature. This has a large number of false positives and is not very accurate [17]. Local thresholding uses local averages to decide whether a feature is present or not. If the current pixel is more than a certain amount above the average then it is a feature. The size of the averaging shrinks as the horizontal lines shrink to cope with perspective.

Different filters have also been used to detect lane markings, steerable being the most common of those. Steerable filters are a combination of rotations of a certain filter through which the response of that filter can be interpolated at any orientation [4] [19]. Reasons for using steerable filters include that the filters can be separated into X and Y components and so the processing can be sped up and the fact that only a few angles of the specific filter are needed to get the whole range of angles and so finding the angle with the highest response is the direction of the lane marking. These filters are used to find angles at which minimum and maximum responses occur or the response at a certain angle. This method works well with lines that are under shadows.

All of the above methods rely on lines that are well defined and have a contrast ratio that is large enough to detect. The problem is that when remarking the lines they are not likely to be in good condition as they are going to be faded and/or dirty from use. It will be much harder to detect lines under these conditions. The radon transform is an integral transform that takes a two dimensional function and breaks it down into a one dimensional view over a set angle or range of angles. Integrating along the length of the line will result in a spike in the output corresponding to the line even if it is somewhat faded or dirty [3].

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Table 3.1 – Advantages and disadvantages of line detection methods. Line Detection

Method

Advantages Disadvantages Global threshold Quick

Simple

Large number of false positives

Positive-Negative Gradient

Fast Doesn’t work for significantly

curved lines Top Hat Works for different thickness

lines

Somewhat resource intensive

Local Threshold Quick Simple

Medium number of false positives

Symmetrical Local Threshold

Fast

Most accurate

Doesn’t work for significantly curved lines

Steerable Filters Wide range of data to work from

Works with shadows

Complex

Resource intensive

Only works for a set width of line Radon Transform Eliminates issues of

dirty/missing lines

Moderately complicated

3.2

Control