Name
Title | ||
|---|---|---|
Ripple-GAN: Lane Line Detection With Ripple Lane Line Detection Network and Wasserstein GAN |
Abstract | ||
|---|---|---|
AbstractWith artificial intelligence technology being advanced by leaps and bounds, intelligent driving has attracted a huge amount of attention recently in research and development. In intelligent driving, lane line detection is a fundamental but challenging task particularly under complex road conditions. In this paper, we propose a simple yet appealing network called Ripple Lane Line Detection Network (RiLLD-Net), to exploit quick connections and gradient maps for effective learning of lane line features. RiLLD-Net can handle most common scenes of lane line detection. Then, in order to address challenging scenarios such as occluded or complex lane lines, we propose a more powerful network called Ripple-GAN, by integrating RiLLD-Net, confrontation training of Wasserstein generative adversarial networks, and multi-target semantic segmentation. Experiments show that, especially for complex or obscured lane lines, Ripple-GAN can produce a superior detection performance to other state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TITS.2020.2971728 | Periodicals |
Keywords | DocType | Volume |
Roads, Gallium nitride, Feature extraction, Semantics, Interference, Training, Image segmentation, Lane line detection, multi-target segmentation, RiLLD-Net, Ripple-GAN | Journal | 22 |
Issue | ISSN | Citations |
3 | 1524-9050 | 3 |
PageRank | References | Authors |
0.40 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Youcheng Zhang | 1 | 3 | 0.73 |
| Lu ZQ | 2 | 47 | 9.45 |
| Dongdong Ma | 3 | 5 | 1.13 |
| Jing-Hao Xue | 4 | 393 | 46.48 |
| QM | 5 | 464 | 72.05 |