@Article{jnm.2020.010062,
AUTHOR = {Xiaoyan Chen, Jiahuan Chen, Zhongcheng Sha},
TITLE = {Edge Detection Based on Generative Adversarial Networks},
JOURNAL = {Journal of New Media},
VOLUME = {2},
YEAR = {2020},
NUMBER = {2},
PAGES = {61--77},
URL = {http://www.techscience.com/JNM/v2n2/39935},
ISSN = {2579-0129},
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.},
DOI = {10.32604/jnm.2020.010062}
}