Study on Optimized Lane Detection Algorithm based on U-Net

DOI

10.2991/iccia-19.2019.35How to use a DOI?

Keywords

Lane detection; lane detection algorithms; U-Net semantics segmentation network model; RESNET; Apollos capes.

Abstract

Lane detection has always been one of the important researches in semantic segmentation, but there are many problems in traditional lane detection algorithms, such as the much larger image pixels, the poor detection effect and so on. Based on the U-Net semantics segmentation network model, this paper redesigns two U-Net optimization network models based on RESNET residual module, and puts forward a series of image preprocessing methods aiming at the dataset’s much larger pixels and some other problems. In the training process, the training data are adjusted Besides, date cleaning, data enhancement, data exposure and other operations are added. The final training model performs well on Apollos capes dataset.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yuanzhou Yao
AU  - Yihang Zhao
AU  - Ao Feng
AU  - Xinyue Su
AU  - Yuhang Yang
AU  - Jie Sun
AU  - Haibo Pu
PY  - 2019/07
DA  - 2019/07
TI  - Study on Optimized Lane Detection Algorithm based on U-Net
BT  - Proceedings of the 3rd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2019)
PB  - Atlantis Press
SP  - 237
EP  - 244
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-19.2019.35
DO  - 10.2991/iccia-19.2019.35
ID  - Yao2019/07
ER  -