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.
Funder
The National Natural Science Foundation of China for Young Scientists
Fundamental and Frontier Research Project of Chongqing
The Foundation and Frontier Research Program of Chongqing Science and Technology Commission
The Humanities and Social Sciences Research Key Program of Chongqing Municipal Education Commission
Publisher
Public Library of Science (PLoS)
Cited by
7 articles.
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