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

PLoS One. 2021 Jul 15;16(7):e0254521. doi: 10.1371/journal.pone.0254521. eCollection 2021.

Abstract

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, we 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 our method performs well compared with the current excellent lane line detection networks.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Attention*
  • Awareness
  • Humans

Grants and funding

JL received the following funding for this study: The National Natural Science Foundation of China for Young Scientists (Grant No. 61502065), the Foundation and Frontier Research Key Program of Chongqing Science and Technology Commission (Grant No. cstc2015jcyjBX0127), the Humanities and Social Sciences Research Key Program of Chongqing Municipal Education Commission (Grant No. 17SKG136), and the Foundation and Frontier Research Program of Chongqing Science and Technology Commission(Grant No. cstc2018jcyjAX0287). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.