Robust Monocular 3D Lane Detection With Dual Attention
- Resource Type
- Conference
- Authors
- Jin, Yujie; Ren, Xiangxuan; Chen, Fengxiang; Zhang, Weidong
- Source
- 2021 IEEE International Conference on Image Processing (ICIP) Image Processing (ICIP), 2021 IEEE International Conference on. :3348-3352 Sep, 2021
- Subject
- Computing and Processing
Signal Processing and Analysis
Interpolation
Three-dimensional displays
Correlation
Lane detection
Image processing
Conferences
Aggregates
3D lane detection
attention mechanism
- Language
- ISSN
- 2381-8549
Getting an accurate estimation of three-dimensional position of the driveable lane is crucial for autonomous driving. In this work, we introduce a novel attention module called Dual Attention (DA) which enables the model to perform robustly and accurately under complicated enviromental conditions. More specifically, the attention mechanism adopts a two-pathway correlated attention method to produce additional features and aggregate globle information. We demonstrate the effectiveness of our method by following and extending recently proposed state-of-the-art 3D lane marking detection methods. Moreover, we use a novel linear-interpolation loss to precisely fit the lane marking. Extensive conducted experiments demonstrate that our methods outperform baseline methods on Apollo synthetic 3D dataset.