Automated Ground Truth Generation for Learning-Based Crack Detection on Concrete Surfaces
This article introduces an automated data-labeling approach for generating crack ground truths (GTs) within concrete images. The main algorithm includes generating first-round GTs, pre-training a deep learning-based model, and generating second-round GTs. On the basis of the generated second-round G...
Guardado en:
Autores principales: | Hsiang-Chieh Chen, Zheng-Ting Li |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/ebcf0a49da7240aa9738c10a4bfdbc2c |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Peculiar aspects of cracking in prestressed reinforced concrete T-beams
por: Vasyl Karpiuk, et al.
Publicado: (2021) -
Crack recognition automation in concrete bridges using Deep Convolutional Neural Networks
por: Zoubir Hajar, et al.
Publicado: (2021) -
Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)
por: Michael Henke, et al.
Publicado: (2021) -
Crack Growth Behavior through Wall Pipes under Impact Load and Hygrothremal Environment
por: Ali Jamal Khaled, et al.
Publicado: (2018) -
Image-Based Automated Width Measurement of Surface Cracking
por: Miguel Carrasco, et al.
Publicado: (2021)