Multiple Lanes Identification using Novel Region-Based Iterative Seed Method
Suvarna Shirke1, R. Udayakumar2

1Suvarna Shirke, Research Scholar, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, India. 

2Dr. R. Udayakumar, Professor and Supervisor, Department of Information Technology, Bharath Institute of Higher Education and Research, Chennai, India.

Manuscript received on 21 September 2019 | Revised Manuscript received on 30 September 2019 | Manuscript Published on 01 October 2019 | PP: 171-177 | Volume-8 Issue-9S4 July 2019 | Retrieval Number: I11250789S419/19©BEIESP | DOI: 10.35940/ijitee.I1125.0789S419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (

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 %.

Keywords: Advanced Driver Assistance System (ADAS), Sparking Method, Region Based Iterative Seed, Segmentation, Multilane Classification, Multilane Detection.
Scope of the Article: Classification