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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: email

Journal of New Media 2020, 2(2), 61-77.


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.


Cite This Article

APA Style
Chen, X., Chen, J., Sha, Z. (2020). Edge detection based on generative adversarial networks. Journal of New Media, 2(2), 61-77.
Vancouver Style
Chen X, Chen J, Sha Z. Edge detection based on generative adversarial networks. J New Media . 2020;2(2):61-77
IEEE Style
X. Chen, J. Chen, and Z. Sha "Edge Detection Based on Generative Adversarial Networks," J. New Media , vol. 2, no. 2, pp. 61-77. 2020.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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