The geometric attention-aware network for lane detection in complex road scenes

Lane detection in complex road scenes is still a challenging task due to poor lighting conditions, interference of irrelevant road markings or signs, etc. To solve the problem of lane detection in the various complex road scenes, the authors proposed a geometric attention-aware network (GAAN) for lane detection. The proposed GAAN adopted a multi-task branch architecture, and used the attention information propagation (AIP) module to perform communication between branches, then the geometric attention-aware (GAA) module was used to complete feature fusion. In order to verify the lane detection effect of the proposed model in this paper, the experiments were conducted on the CULane dataset, TuSimple dataset, and BDD100K dataset. The experimental results show that the authors method performs well compared with the current excellent lane line detection networks.

• Record URL:
  https://doi.org/10.1371/journal.pone.0254521
• Supplemental Notes:
  • © 2021 JianWu Long, et al.
• Authors:
  • Long, JianWu
  • Yan, ZeRan
  • Peng, Lang
  • Li, Tong
• Publication Date: 2021

Language

• English

Media Info

• Media Type: Web
• Pagination: e0254521
• Serial:
  • PLoS One
  • Volume: 16
  • Issue Number: 7
  • Publisher: Public Library of Science
  • EISSN: 1932-6203
  • Serial URL: https://journals.plos.org/plosone/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Data files; Detectable warning surfaces; Experiments; Lane lines; Machine learning; Neural networks; Vehicle safety; Visibility distance
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01777629
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 23 2021 3:26PM