A novel region-based iterative seed method for the detection of multiple lanes

ABSTRACT Most of the global automotive companies have been paid great efforts for reducing the accidents by developing an Advanced Driver Assistance System (ADAS) as well as autonomous vehicles. Lane detection is essential for both autonomous driving and ADAS because the vehicles must follow the lane. Detection of the lane is very challenging because of the varying road conditions. Lane detection has attracted the attention of the computer vision community for several decades. Essentially, lane detection is a multi-feature detection problem that has become a real challenge for computer vision and machine learning techniques. This paper presents a region-based segmentation based on iterative seed method for multi-lane detection. Here, the detection of multi-lanes is done after the segmentation, which is highly efficient and improves the computing speed. In the proposed region-based segmentation method, the segmentation of lanes from the roads is carried out by selecting the target grids, after partitioning the input image into grids. Then, based on the distance measure, the optimal segments are chosen by an iterative procedure. The performance of the proposed region-based iterative seed method is evaluated using detection accuracy, sensitivity, and specificity, where it has the maximum detection accuracy of 98.89%.