E3S Web Conf.
Volume 136, 20192019 International Conference on Building Energy Conservation, Thermal Safety and Environmental Pollution Control (ICBTE 2019)
|Number of page(s)||5|
|Section||Urban Public Safety|
|Published online||10 December 2019|
Traffic marking recognition based on generating antagonistic neural network
1 Highway college, Chang’an University, Xi’an, Shaanxi, 710064, China
2 Highway college, Chang’an University, Xi’an, Shaanxi, 710064, China
* Corresponding author’s e-mail: email@example.com
This paper presents a method of extracting traffic lines from image images by GAN. Compared with the traditional image detection methods, the counter neural network does not need repeated sampling of Markov chain and adopts the method of backward propagation. Therefore, when detecting the image, GAN do not need to be updated with samples; it can produce better quality samples, express more clearly. Experimental results show that the method has strong generalization ability, fast recognition speed and high accuracy.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.