Cargando…
Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks
Constant monitoring of road surfaces helps to show the urgency of deterioration or problems in the road construction and to improve the safety level of the road surface. Conditional generative adversarial networks (cGAN) are a powerful tool to generate or transform the images used for crack detectio...
Autores principales: | Kyslytsyna, Anastasiia, Xia, Kewen, Kislitsyn, Artem, Abd El Kader, Isselmou, Wu, Youxi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587934/ https://www.ncbi.nlm.nih.gov/pubmed/34770711 http://dx.doi.org/10.3390/s21217405 |
Ejemplares similares
-
Pavement Cracks Segmentation Algorithm Based on Conditional Generative Adversarial Network
por: Kang, Jie, et al.
Publicado: (2022) -
Road Topology Refinement via a Multi-Conditional Generative Adversarial Network
por: Zhang, Yang, et al.
Publicado: (2019) -
Biconditional Generative Adversarial Networks for Multiview Learning with Missing Views
por: Doinychko, Anastasiia, et al.
Publicado: (2020) -
Fast Simulation Using Generative Adversarial Network in LHCB
por: Maevskiy, Artem
Publicado: (2019) -
Super-Resolution Reconstruction Method of Pavement Crack Images Based on an Improved Generative Adversarial Network
por: Yuan, Bo, et al.
Publicado: (2022)