Title
Empower parameterized generative adversarial networks using a novel particle swarm optimizer: algorithms and applications
Abstract
In this paper, a novel parameterized generative adversarial network (GAN) is proposed where the parameters are introduced to enhance the performance of image segmentation. The developed algorithm is applied to the image-based crack detection problem on the thermal data obtained through the non-destructive testing process. A new regularization term, which contains three tunable hyperparameters, embedded into the objective function of the GAN in order to improve the contrast ratio of certain areas of the image so as to benefit the crack detection process. To automate the selection of the optimal hyperparameters of the GAN, a new particle swarm optimization (PSO) algorithm is put forward where a neighborhood-based velocity updating strategy is developed for the purpose of thoroughly exploring the problem space. The proposed PSO-based GAN algorithm is shown to 1) work well in detecting cracks on the thermal data generated by the eddy current pulsed thermography technique; and 2) outperforms other conventional GAN algorithms.
Year
DOI
Venue
2022
10.1007/s13042-021-01440-3
International Journal of Machine Learning and Cybernetics
Keywords
DocType
Volume
Generative adversarial network, Particle swarm optimization, Hyperparameter optimization, Crack detection, Non-destructive testing, Thermal image analysis
Journal
13
Issue
ISSN
Citations 
4
1868-8071
0
PageRank 
References 
Authors
0.34
1
6
Name
Order
Citations
PageRank
Lulu Tian101.35
Zidong Wang211003578.11
Weibo Liu352016.88
Yuhua Cheng413633.41
Fuad E. Alsaadi500.34
Xiaohui Liu65042269.99