Accurate lane detection with atrous convolution and spatial pyramid pooling for autonomous driving

Yuxiang Sun, Lujia Wang, Yongquan Chen, Ming Liu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Lane detection is a fundamental capability for autonomous driving. Many effective lane detection algorithms based on traditional computer vision and recent deep learning technologies have been proposed. However, the current state-of-the-art lane detection accuracy is still not satisfactory for realizing fully autonomous driving. Thus, this paper proposes a new lane detection network using atrous convolution and spatial pyramid pooling techniques to improve the lane detection accuracy. We address the detection problem with pixel-wise semantic segmentation. Our network consists of one encoder and two decoders, which outputs a binary segmentation map and an embedded feature map, respectively. The embedded feature map is employed for clustering algorithms to separate segmented lane pixels into different lanes. The experimental results on the public Tusimple dataset show that our network outperforms the state-of-the-arts.

Original languageEnglish
Title of host publicationIEEE International Conference on Robotics and Biomimetics, ROBIO 2019
PublisherIEEE
Pages642-647
Number of pages6
ISBN (Electronic)9781728163215
DOIs
Publication statusPublished - Dec 2019
Event2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019 - Dali, China
Duration: 6 Dec 20198 Dec 2019

Publication series

NameIEEE International Conference on Robotics and Biomimetics, ROBIO 2019

Conference

Conference2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019
Country/TerritoryChina
CityDali
Period6/12/198/12/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture
  • Mechanical Engineering
  • Control and Optimization

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