Road crack segmentation using an attention residual U-Net with generative adversarial
This paper proposed an end-to-end road crack segmentation model based on attention mechanism and deep FCN with generative adversarial learning. We create a segmentation network by introducing a visual attention mechanism and residual module to a fully convolutional network(FCN) to capture richer loc...
Guardado en:
Autores principales: | Xing Hu, Minghui Yao, Dawei Zhang |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
AIMS Press
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/ccc0569333544ed7a9cc36fc49156950 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks
por: Anastasiia Kyslytsyna, et al.
Publicado: (2021) -
Second-order ResU-Net for automatic MRI brain tumor segmentation
por: Ning Sheng, et al.
Publicado: (2021) -
Accurate pancreas segmentation using multi-level pyramidal pooling residual U-Net with adversarial mechanism
por: Meiyu Li, et al.
Publicado: (2021) -
Adversarial Learning with Bidirectional Attention for Visual Question Answering
por: Qifeng Li, et al.
Publicado: (2021) -
GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention
por: Udit Sharma, et al.
Publicado: (2021)