Detection of unstructured roads from a single image for autonomous navigation applications

Kodeeswari Manoharan; Philemon Daniel

doi:10.1117/1.JEI.27.4.043049

20 August 2018 Detection of unstructured roads from a single image for autonomous navigation applications

Kodeeswari Manoharan, Philemon Daniel

Author Affiliations +

Funded by: Government of India

Journal of Electronic Imaging, Vol. 27, Issue 4, 043049 (August 2018). https://doi.org/10.1117/1.JEI.27.4.043049

Abstract

With the initiation of an advanced driver assistance system in high to low end segment cars, traffic safety has obtained a huge significance, and focus on automotive technology has been made to augment the safety of the drivers, pedestrians, and passengers on traffic scenes. This article explores the possibility of computer vision techniques to detect any arbitrary hilly unstructured road from a given single image. The proposed technique identifies the majority of road pixels in an image captured using an onboard camera and covers most drivable area in front of the automobile. It is identified to be a challenge to detect these roads as they may not be smooth, structured, or they do not have any vivid edges. An approach based on color cues for identifying these unstructured roads from the captured image is proposed and processing is carried out on the region of interest to speed up the computation for road area detection. The proposed method utilizes the new effective value computed by modifying the intensity information to identify the probability of every picture element to be on road or off-road segment. The presented algorithm has been verified by testing on mountainous Himalayan roads and demonstrates that it is more efficient in detecting unstructured road boundaries in challenging scenarios.

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Kodeeswari Manoharan and Philemon Daniel "Detection of unstructured roads from a single image for autonomous navigation applications," Journal of Electronic Imaging 27(4), 043049 (20 August 2018). https://doi.org/10.1117/1.JEI.27.4.043049

Received: 1 February 2018; Accepted: 30 July 2018; Published: 20 August 2018

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