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

• Published in: PloS one Vol. 16; no. 7; p. e0254521
• Main Authors: Long, JianWu, Yan, ZeRan, Peng, Lang, Li, Tong
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 15.07.2021; Public Library of Science (PLoS)
• Subjects: Analysis; Biology and Life Sciences; China; Classification; Computer and Information Sciences; Computer science; Datasets; Engineering and Technology; Modules; Neural networks; Physical Sciences; Research and Analysis Methods; Road construction; Roads; Semantics; Social Sciences; Streets; Structure; Traffic; Traffic signs; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0254521

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.