.png){kind=icon}
返回
Multi-task deep learning for crack segmentation and quantification in RC structures
.png){kind=icon}
PRE
AI
DOI：10.1016/j.autcon.2024.105599.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Crack width is frequently used for damage assessment in reinforced concrete (RC) structures. Recently, deep learning (DL) based approaches have been proposed for crack detection and segmentation. However, most of them focus on extracting the crack regions, followed by width calculation using conventional methods. To address this issue, we propose a multi-task DL model to predict crack segmentation and crack centerline simultaneously. Effects of loss functions are investigated, and state-of-the-art segmentation models are employed as baselines. Results show that the proposed multi-task U-Net model enhances the estimation in crack centerline and outperforms the baselines by more than 2% in width quantification. Moreover, we propose a computer vision (CV) based approach to calculate crack width when camera shooting angle is not perpendicular to target surface. 3D reconstruction and plane fitting are incorporated to correct distortion, and images obtained from non-vertical capturing are used to demonstrate the robustness of the proposed approach.
Keyword:
Crack segmentation
Crack width quantification
Multi-task deep learning
3D reconstruction
Plane fitting
期刊
IF:
11.5
论文数:
6.3K
被引数:
4.2W
机构
引用论文
An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement沥青路面自动像素级裂缝检测与量化的综合方法
PREAI
分享收藏分享收藏A UAV-based machine vision method for bridge crack recognition and width quantification through hybrid feature learning基于无人机的混合特征学习桥梁裂缝识别与宽度量化的机器视觉方法
PREAI
分享收藏DeepCrack: A deep hierarchical feature learning architecture for crack segmentation.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Crack detection and quantification for concrete structures using UAV and transformer基于无人机和变压器的混凝土结构裂缝检测与量化
PREAI
分享收藏
查看更多内容