A real‐time lane detection network using two‐directional separation attention

Author:

Zhang Lu123,Jiang Fengling4,Yang Jing15,Kong Bin13,Hussain Amir6

Affiliation:

1. Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China

2. Science Island Branch Graduate School of USTC Hefei China

3. Anhui Engineering Laboratory for Intelligent Driving Technology and Application Hefei China

4. School of Computing and Artificial Intelligence Hefei Normal University Hefei China

5. School of Artificial Intelligence and Big Data Hefei University Hefei China

6. School of Computing Edinburgh Napier University Scotland UK

Abstract

AbstractReal‐time network by adopting attention mechanism is helpful for enhancing lane detection capability of autonomous vehicles. This paper proposes a real‐time lane detection network (TSA‐LNet) that incorporates a lightweight network (LNet) and a two‐directional separation attention (TSA) to enhance the lane detection capability of autonomous vehicles. By adopting the attention mechanism, the real‐time performance and detection accuracy are significantly improved. Specifically, LNet employs symmetry layer to drastically reduce the number of parameters and the network's running time. TSA infers the attention map along two separate directions, transverse and longitudinal, and performs adaptive feature refinement by multiplying the attention map with the input feature map. TSA can be integrated into LNet to capture the local textural and global contextual information of lanes without increasing the processing time. Results on popular benchmarks demonstrate that TSA‐LNet achieves outstanding detection accuracy and faster speed (6.99 ms per image). Additionally, TSA‐LNet exhibits excellent robustness in real‐world scenarios.

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Civil and Structural Engineering,Building and Construction

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