Zhang, Y;
Lu, Z;
Ma, D;
Xue, J-H;
Liao, Q;
(2020)
Ripple-GAN: Lane line detection with Ripple Lane Line Detection Network and Wasserstein GAN.
IEEE Transactions on Intelligent Transportation Systems
10.1109/TITS.2020.2971728.
(In press).
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Abstract
With 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.
Type: | Article |
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Title: | Ripple-GAN: Lane line detection with Ripple Lane Line Detection Network and Wasserstein GAN |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/TITS.2020.2971728 |
Publisher version: | https://doi.org/10.1109/TITS.2020.2971728 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10096240 |
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