Multiple lanes identification using novel region-based iterative seed method

Abstract

Now a days, in each year thousands of car accidents occurs in India. Therefore, most of the automobile companies tries to give best Advanced Driver Assistance System (ADAS) to avoid the accidents. The lane detection is one of the approach to design the ADAS, if the vehicles follows the lane then there is less chance to get an accident. The detected information of lane path is used for controlling the vehicles and giving alerts to drivers. Therefore most of the researchers are attracted towards this field. But, due to the varying road conditions, it is very difficult to detect the lane. The computer vision and machine learning approaches are presents in most of the articles. In this paper, a seed method is designed for the road picture segmentation for the multi-lane detection. The sparking method is applied to the segmented image to increase the speed of computer. In this proposed method, the target grids are selected form the road lane. Distance is calculated for road and lane. Based on the distance measure, the optimal segments are chosen, following an iterative procedure. The accuracy, sensitivity and specificity are considered for the performance point of view for this paper. The calculated maximum detected accuracy is 98.89 %.