EFFICIENT LANE DETECTION BASED ON ARTIFICIAL NEURAL NETWORKS

Arce, F.; Zamora, E.; Hernández, G.; Sossa, H.

Lane detection is a problem that has attracted in the last years the attention of the computer vision community. Most of approaches used until now to face this problem combine conventional image processing, image analysis and pattern classification techniques. In this paper, we propose a methodology based on so-called Ellipsoidal Neural Networks with Dendritic Processing (ENNDPs) as a new approach to provide a solution to this important problem. The functioning and performance of the proposed methodology is validated with a real video taken by a camera mounted on a car circulating on urban highway of Mexico City.

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Arce, F. / Zamora, E. / Hernández, G. / et al: EFFICIENT LANE DETECTION BASED ON ARTIFICIAL NEURAL NETWORKS. 2017. Copernicus Publications.

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