.png){kind=icon}
返回
Bridge inspection component registration for damage evolution
.png){kind=icon}
PRE
AI
DOI：10.1177/14759217221083647.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
There have been great advances in bridge inspection damage detection involving the use of deep learning models. However, automated detection models currently fall short of giving an inspector an understanding of how the damage has progressed from one inspection to the next. The rate-of-change of the damage is a critical piece of information used by engineers to determine appropriate maintenance and rehabilitation actions to prevent structural failures. We propose a simple methodology for registering two bridge inspection videos or still images, collected at different stages of deterioration, so that trained model predictions may be directly measured and damage progression compared. The changes may be documented and presented to the inspector so that they may quickly evaluate key interest regions in the inspection video or image. Three approaches referred to as rigid, deformable, and hybrid image registration methods were experimentally tested and evaluated based on their ability to preserve the geometric characteristics of the referenced image. It was found in all experiments that the rigid, homography-based transformations performed the best for this application over a state-of-the-art deformable registration method, RANSAC-Flow.
Keyword:
Bridge inspection
image registration
change detection
corrosion
damage detection
machine learning
deep learning
condition states
steel bridge
期刊
S
IF:
5.7
论文数:
2.3K
被引数:
1.1W
机构
引用论文
.png){kind=icon}
分享收藏AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning PipelineAutoMorph: 通过深度学习管道自动量化视网膜血管形态
OAAI
OAAI分享收藏The South African form of severe and complicated canine babesiosis: Clinical advances 1994–2004
PREAI
分享收藏
查看更多内容