Lane Line Detection via Deep Learning Based- Approach Applying Two Types of Input into Network Model

Abstract

Lane line detection is one of the important modules for Advanced Driver-Assistance System (ADAS) that are applied in the autonomous vehicle. This module work by exhibit the position of the road lane marking and providing the details of the geometrical features of the lane line structures into the intelligent system. This paper proposes the lane line marking detection using Fully Convolutional Neural Network (FCN) model by investigating the two types of input fed into the networks. RGB- channel (Red, Green, Blue) and Canny edge were used as the inputs to develop in the FCN model. The FCN approach has been proposed as one of the solution methods in mitigating the road lane detection issues due to its great performance in the application of objects detection in image or video. Previously, the RGB-channel is widely applied in the deep learning method meanwhile, the Canny-edge input has not been applied yet in the deep learning method. Therefore, this study investigates the further performance of this model by applying the canny edge as addition input besides applying only the RGB-channel. The data collections were acquired from real-time data collection. The result shows that the FCN model with the canny edge achieved a slight improvement with 96% compared to FCN with the RGB-channel with 92%.