Cargando…
An Automated Image-Based Multivariant Concrete Defect Recognition Using a Convolutional Neural Network with an Integrated Pooling Module
Buildings and infrastructure in congested metropolitan areas are continuously deteriorating. Various structural flaws such as surface cracks, spalling, delamination, and other defects are found, and keep on progressing. Traditionally, the assessment and inspection is conducted by humans; however, du...
Autores principales: | Kim, Bubryur, Choi, Se-Woon, Hu, Gang, Lee, Dong-Eun, Serfa Juan, Ronnie O. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105078/ https://www.ncbi.nlm.nih.gov/pubmed/35590810 http://dx.doi.org/10.3390/s22093118 |
Ejemplares similares
-
Multivariate Analysis of Concrete Image Using Thermography and Edge Detection
por: Kim, Bubryur, et al.
Publicado: (2021) -
Automatic recognition of murmurs of ventricular septal defect using convolutional recurrent neural networks with temporal attentive pooling
por: Wang, Jou-Kou, et al.
Publicado: (2020) -
Performance Comparison of Multiple Convolutional Neural Networks for Concrete Defects Classification
por: Arafin, Palisa, et al.
Publicado: (2022) -
Rapid Post-Earthquake Structural Damage Assessment Using Convolutional Neural Networks and Transfer Learning
por: Ogunjinmi, Peter Damilola, et al.
Publicado: (2022) -
Chip Appearance Defect Recognition Based on Convolutional Neural Network
por: Wang, Jun, et al.
Publicado: (2021)