Hidden danger identification of slope based on improved semi-supervised GAN

Yao Zhenyu; Wu Xiaozhong; Gan Xing; Lu Lin

doi:10.1117/12.3026791

27 March 2024 Hidden danger identification of slope based on improved semi-supervised GAN

Yao Zhenyu, Wu Xiaozhong, Gan Xing, Lu Lin

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 1310520 (2024) https://doi.org/10.1117/12.3026791
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

Aiming at the problem that the discriminator of original conditional generative adversarial network cannot classify real samples, a semi-supervised generative adversarial network structure is proposed to realize the classification function of real samples. Considering that the traditional semi-supervised generation adversarial network is too rough to evaluate the generator, the loss function during the training of the generator is improved, and the generator can generate more realistic samples. The mature discriminator corresponding to the improved semi-supervised generation countermeasures network is deployed in the vibration monitoring terminal, and the high accuracy is obtained, which can effectively identify the hidden danger information of slope when the static, vehicle passing and impact events occur.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yao Zhenyu, Wu Xiaozhong, Gan Xing, and Lu Lin "Hidden danger identification of slope based on improved semi-supervised GAN", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 1310520 (27 March 2024); https://doi.org/10.1117/12.3026791

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available