A Vision-based Lane Detection Technique using Deep Neural Networks and Temporal Information

Chun-Ke Chang, Huei-Yung Lin

2022

Abstract

With the advances of driver assistance technologies, more and more people begin to pay attentions on traffic safety. Among various vehicle subsystems, the lane detection module is one of the important parts of advanced driver assistance system (ADAS). Traditional lane detection techniques use machine vision algorithms to find straight lines in road scene images. However, it is difficult to identify straight or curve lane markings in complex environments. This paper presents a lane detection technique based on the deep neural network. It utilizes the 3D convolutional network with the incorporation of temporal information to the network structure. Two well-known lane detection network structures, PINet and PolyLaneNet, are improved by integrating 3D ResNet50. In the experiments, the accuracy is greatly improved for the applications to a variety of different complex scenes.

Paper Citation

in Harvard Style

Chang C. and Lin H. (2022). A Vision-based Lane Detection Technique using Deep Neural Networks and Temporal Information. In Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-573-9, pages 172-179. DOI: 10.5220/0010973200003191

in Bibtex Style

@conference{vehits22,
author={Chun-Ke Chang and Huei-Yung Lin},
title={A Vision-based Lane Detection Technique using Deep Neural Networks and Temporal Information},
booktitle={Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2022},
pages={172-179},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010973200003191},
isbn={978-989-758-573-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - A Vision-based Lane Detection Technique using Deep Neural Networks and Temporal Information
SN - 978-989-758-573-9
AU - Chang C.
AU - Lin H.
PY - 2022
SP - 172
EP - 179
DO - 10.5220/0010973200003191