Open Access
ARTICLE
Edge Detection Based on Generative Adversarial Networks
Xiaoyan Chen, Jiahuan Chen*, Zhongcheng Sha
Nanjing University of Information Science and Technology, Nanjing, 210044, China
* Corresponding Author: Jiahuan Chen. Email:
Journal of New Media 2020, 2(2), 61-77. https://doi.org/10.32604/jnm.2020.010062
Received 10 February 2020; Accepted 21 March 2020; Issue published 21 August 2020
Abstract
Aiming at the problem that the detection effect of traditional edge
detection algorithm is not good, and the problem that the existing edge detection
algorithm based on convolution network cannot solve the thick edge problem
from the model itself, this paper proposes a new edge detection method based on
the generative adversarial network. The confrontation network consists of
generator network and discriminator network, generator network is composed of
U-net network and discriminator network is composed of five-layer convolution
network. In this paper, we use BSDS500 training data set to train the model.
Finally, several images are randomly selected from BSDS500 test set to compare
with the results of traditional edge detection algorithm and HED algorithm. The
results of BSDS500 benchmark test show that the ODS and OIS indices of the
proposed method are 0.779 and 0.782 respectively, which are much higher than
those of traditional edge detection algorithms, and the indices of HED algorithm
using non-maximum suppression are similar.
Keywords
Cite This Article
X. Chen, J. Chen and Z. Sha, "Edge detection based on generative adversarial networks,"
Journal of New Media, vol. 2, no.2, pp. 61–77, 2020. https://doi.org/10.32604/jnm.2020.010062