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FIGURE 4 | The image in its original form, represented in grayscale. FIGURE 3 | The image in its original form. Accumulator

Figure 4 | The image in its original form, represented in grayscale. FIGURE 3 | The image in its original form. Accumulator

Related Figures (18)

FIGURE 1 | The flowchart for the lane-line RTRD algorithm. To generate the final edge map, a thresholding step using either a small filter or a big filter is applied to the non- maximum suppressed acquired picture. Strong edges are discovered when pixel magnitude values exceed the high threshold; weak edges are recognized when they fall between Image space
FIGURE 1 | The flowchart for the lane-line RTRD algorithm. To generate the final edge map, a thresholding step using either a small filter or a big filter is applied to the non- maximum suppressed acquired picture. Strong edges are discovered when pixel magnitude values exceed the high threshold; weak edges are recognized when they fall between Image space
FIGURE 2 | The lane-line RTRD algorithm detects and identifies the boundaries of the detected lane lines.
FIGURE 2 | The lane-line RTRD algorithm detects and identifies the boundaries of the detected lane lines.
FIGURE 6 | The picture with Canny edge detection. FIGURE 5 | After using a Gaussian blur filter, the captured picture is displayed in grayscale.
FIGURE 6 | The picture with Canny edge detection. FIGURE 5 | After using a Gaussian blur filter, the captured picture is displayed in grayscale.
FIGURE 7 | The pictures after creating lane lines using parts of Hough lines.
FIGURE 7 | The pictures after creating lane lines using parts of Hough lines.
TABLE 3 | Hough transform parameters.
TABLE 3 | Hough transform parameters.
TABLE 2 | Region of Interest (ROI) vertices.
TABLE 2 | Region of Interest (ROI) vertices.
TABLE 1 | Canny Edge Algorithm Parameters.
TABLE 1 | Canny Edge Algorithm Parameters.
FIGURE 8 | The picture after Canny edge detection and region of interest.
FIGURE 8 | The picture after Canny edge detection and region of interest.
FIGURE 9 | The pictures after creating lane lines using parts of Hough lines.
FIGURE 9 | The pictures after creating lane lines using parts of Hough lines.
TABLE 4 | The resulting finite impulse response filter coefficients and the corresponding parameters. TABLE 5 | The computation speed results for the Lane-Line RTRD algorithm.
TABLE 4 | The resulting finite impulse response filter coefficients and the corresponding parameters. TABLE 5 | The computation speed results for the Lane-Line RTRD algorithm.
Repeat steps 1-6 for each frame of a video stream to perform real-time lane identification.
Repeat steps 1-6 for each frame of a video stream to perform real-time lane identification.
FIGURE 10 | The tested image featured a combination of lane lines are solid yellow and dotted white, with the presence of cars in the right lane.
FIGURE 10 | The tested image featured a combination of lane lines are solid yellow and dotted white, with the presence of cars in the right lane.
FIGURE 11 | The picture contains solid yellow and dotted white lane lines segments. The lane lines are in a left-turned lane, and there are no cars in the image.
FIGURE 11 | The picture contains solid yellow and dotted white lane lines segments. The lane lines are in a left-turned lane, and there are no cars in the image.
FIGURE 13 | The test image showcases segments of lane lines are solid yellow and dotted white, representing a left lane without any cars.
FIGURE 13 | The test image showcases segments of lane lines are solid yellow and dotted white, representing a left lane without any cars.
FIGURE 12 | The picture displays segments of lane lines are solic yellow and dotted white.
FIGURE 12 | The picture displays segments of lane lines are solic yellow and dotted white.
FIGURE 14 | The presence of shadow patterns can result in the inaccurate detection of lane lines.
FIGURE 14 | The presence of shadow patterns can result in the inaccurate detection of lane lines.
Related topics:
Hough Transform
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