Abstract
Lane detection is a key technology in automatic driving systems. Aiming at the problems of low accuracy and susceptible to interference in the traditional detection algorithm as for yellow lane lines, and poor recognition of curved lane lines by the traditional Hough transform. In this paper, a lane line detection method based on machine vision is proposed. Firstly, the white lane lines are identified in the gray space, the yellow lane lines are identified in the HSV space and the yellow lane lines and white lane lines are then merged. The interference is reduced by dividing the region of interest and Gaussian filtering. Then, the edge of the lane line is identified by edge detection and the image is transformed into an aerial view by inverse perspective transformation. Finally, the sliding window polynomial fitting method is adopted to fit the lane lines. At the same time, a lane departure warning method based on calculating curve curvature and center deviation is proposed.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.