This paper presents a novel algorithm for nighttime detection of the lane markers painted on a road at night. First of all, the proposed algorithm uses neighborhood average filtering, 8-directional Sobel operator and thresholding segmentation based on OTSU's to handle raw lane images taken from a digital CCD camera. Secondly, combining intensity map and gradient map, we analyze the distribution features of pixels on boundaries of lanes in the nighttime and construct 4 feature sets for these points, which are helpful to supply with sufficient data related to lane boundaries to detect lane markers much more robustly. Then, the searching method in multiple directions- horizontal, vertical and diagonal directions, is conducted to eliminate the noise points on lane boundaries. Adapted Hough transformation is utilized to obtain the feature parameters related to the lane edge. The proposed algorithm can not only significantly improve detection performance for the lane marker, but it requires less computational power. Finally, the algorithm is proved to be reliable and robust in lane detection in a nighttime scenario.
© 2013 Optical Society of Korea
(110.2960) Imaging systems : Image analysis
(150.0150) Machine vision : Machine vision
(100.3008) Image processing : Image recognition, algorithms and filters
(100.4999) Image processing : Pattern recognition, target tracking
Original Manuscript: November 16, 2012
Revised Manuscript: March 21, 2013
Manuscript Accepted: March 25, 2013
Published: April 25, 2013
Feng You, Ronghui Zhang, Lingshu Zhong, Haiwei Wang, and Jianmin Xu, "Lane Detection Algorithm for Night-time Digital Image Based on Distribution Feature of Boundary Pixels," J. Opt. Soc. Korea 17, 188-199 (2013)
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