Drivable Road Corridor Detection using Flood Fill Road Detection Algorithm
Karthik Shetty1, Pratik Kanani2

1Karthik Shetty, Department of Computer Engineering, Dwarkadas J. Sanghvi College of Engineering, University of Mumbai, India.
2Pratik Kanani, Department of Computer Engineering, Dwarkadas J. Sanghvi College of Engineering, University of Mumbai, India.
Manuscript received on December 01, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 5011-5014 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B2834129219/2019©BEIESP | DOI: 10.35940/ijeat.B2834.129219
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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 (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Current image processing techniques for drivable road detection make use of lane markings. However, most roads lack lane markings which make such techniques obsolete. For such conditions, an image processing technique is required which identifies the boundaries of the road based on the color differences between the road and the surroundings. This paper proposes a flood fill road detection approach in which we first analyze a sample of the road and compute its RGB pixel distribution. The pixel range is used to detect the other road pixels in the image. Edge detection algorithms are then applied on the detected road to give road edge. It classifies the road on the basis of the visible differences between the road and its neighborhood. It allows for subtle color differences on the road surface, and unlike a color mask, due to the inherent growing nature of a flood fill algorithm, it does not detect neighborhood elements beyond the boundary having features similar to the road. This technique also manages to detect any obstructions on the road as opposed to other edge detection algorithms. We also propose methods to enable quick computation of otherwise expensive flood-fill algorithm. The method was tested on both marked and unmarked lanes and produced satisfying results for both images and videos.
Keywords: Road detection, image processing, flood-fill algorithm