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...
Enregistré dans:
Auteurs principaux: | Hsiang-Chieh Chen, Zheng-Ting Li |
---|---|
Format: | article |
Langue: | EN |
Publié: |
MDPI AG
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/ebcf0a49da7240aa9738c10a4bfdbc2c |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Peculiar aspects of cracking in prestressed reinforced concrete T-beams
par: Vasyl Karpiuk, et autres
Publié: (2021) -
Crack recognition automation in concrete bridges using Deep Convolutional Neural Networks
par: Zoubir Hajar, et autres
Publié: (2021) -
Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)
par: Michael Henke, et autres
Publié: (2021) -
Crack Growth Behavior through Wall Pipes under Impact Load and Hygrothremal Environment
par: Ali Jamal Khaled, et autres
Publié: (2018) -
Image-Based Automated Width Measurement of Surface Cracking
par: Miguel Carrasco, et autres
Publié: (2021)